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HCLS Benchmarks Implementation Order

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Released: April 25, 2012

Federal Communications Commission

DA 12-646

Before the

Federal Communications Commission

Washington, D.C. 20554

In the Matter of
)
)

Connect America Fund
)
WC Docket No. 10-90
)
High-Cost Universal Service Support
)
WC Docket No. 05-337

ORDER

Adopted: April 25, 2012

Released: April 25, 2012

By the Chief, Wireline Competition Bureau:

I.

INTRODUCTION

1.
In the USF/ICC Transformation Order, the Commission comprehensively
reformed universal service funding for high-cost, rural areas, adopting fiscally responsible,
accountable, incentive-based policies to preserve and advance voice and broadband service while
ensuring fairness for consumers who pay into the universal service fund (Fund).1 As a
component of those reforms, the Commission adopted a benchmarking rule intended to moderate
the expenses of those rate-of-return carriers with very high costs compared to their similarly
situated peers, while further encouraging other rate-of-return carriers to advance broadband
deployment.2 In this order, we adopt the specific methodology for establishing such limits or
“benchmarks” for high cost loop support (HCLS).3
2.
The Commission’s benchmark rule responded to problematic incentives and
inequitable distribution of support created by the prior rules. Under the prior rules, some carriers
with high costs may have had up to 100 percent of their expenditures on loop costs reimbursed
from the federal universal service fund. Because, prior to the USF/ICC Transformation Order,
these carriers generally faced no overall limits on their expenditures, our rules gave carriers
incentives to increase loop costs with little regard to efficiency or the burden on the Fund, and


1 See Connect America Fund; A National Broadband Plan for Our Future; Establishing Just and
Reasonable Rates for Local Exchange Carriers; High-Cost Universal Service Support; Developing a
Unified Intercarrier Compensation Regime; Federal-State Joint Board on Universal Service; Lifeline and
Link-Up; Universal Service Reform—Mobility Fund
; WC Docket Nos. 10-90, 07-135, 05-337, 03-109, CC
Docket Nos. 01-92, 96-45, GN Docket No. 09-51, WT Docket No. 10-208, Report and Order and Further
Notice of Proposed Rulemaking, 26 FCC 17663 (2011) (USF/ICC Transformation Order and FNPRM);
pets. for review pending sub nom. In re: FCC 11-161, No. 11-9900 (10th Cir. filed Dec. 8, 2011).
2 Id. at 17741-47, paras. 210-26.
3 Specifically, the methodology implements the Commission’s rule, adopted in the USF/ICC
Transformation Order
, to limit reimbursable capital and operating costs for purposes of determining HCLS
by using benchmarks for reasonable costs among similarly situated rate-of-return carriers. See id. at 17745,
para. 220.

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without regard to whether a lesser amount would be sufficient to provide supported services to
their customers. Moreover, because HCLS overall is capped, carriers that did take measures to
reduce costs to operate more efficiently lost support to their peers that increased costs.
3.
The benchmarking rule adopted by the Commission addresses these problems by,
for the first time, placing reasonable overall limits on costs eligible for reimbursement through
HCLS and redistributing freed-up HCLS to carriers that stay within these limits to allow for new
broadband investment.4 The Commission sought comment on a specific methodology to limit
reimbursable capital and operating costs within HCLS and directed the Wireline Competition
Bureau (Bureau) to finalize a methodology after receiving public input in response to the
proposal.5
4.
The methodology we adopt today, which is described in more detail in the
attached technical appendix,6 builds on the analysis proposed in the USF/ICC Transformation
FNPRM
,7 but also includes several changes in response to the comments from two peer reviewers
and interested parties and based on further analysis by the Bureau.8 These changes significantly
improve the methodology while redistributing funding to a greater number of carriers to support
continued broadband investment. We now estimate that support to approximately 100 study areas
with very high costs relative to similarly situated peers will be limited, while approximately 500
study areas will receive additional, redistributed support to fund new broadband investment.9
5.
In view of the Commission’s intent to “phase in reform with measured but
certain transitions,”10 we will phase in the application of these limits. As directed by the
Commission, we are providing public notice in Appendix B of this order regarding the updated
company-specific capped values that will be used in the HCLS formula. These capped values
(which we also refer to as limits or benchmarks) will be used from July 1, 2012 through
December 31, 2012,11 in place of an individual company’s actual cost data for those rate-of-return
cost companies whose costs exceed the caps.12 While the HCLS benchmarks will be
implemented beginning July 1, 2012, we will not reduce support amounts immediately by the full


4 Id.
5 See id. at 17743-47, paras. 214-26, 18059-62, paras. 1079-88, 18285-94, App. H.
6 See infra Appendix A.
7 See USF/ICC Transformation Order and FNPRM, 26 FCC Rcd at 18059-62, paras. 1079-88, 18285-94,
App. H.
8 See Letter from Patrick Halley, FCC, to Marlene Dortch, FCC, WC Docket Nos. 10-90, 07-135, 05-337,
GN Docket No. 09-51, CC Docket Nos. 01-92, 96-45, 03-109, at Apps. B & C (filed Mar. 9, 2012) (Sanyal
Peer Review and Waldon Peer Review, respectively).
9 Based on the methodology proposed in the USF/ICC Transformation Order and FNPRM, the
Commission estimated that support to 280 rate-of-return cost study areas would be reduced and that 340
rate-of-return cost study areas would receive additional support. USF/ICC Transformation Order and
FNPRM
, 26 FCC Rcd at 18061, para. 1084.
10 Id. at 17671, para. 11.
11 See infra section III.G for a detailed discussion of how the transition will be implemented.
12 USF/ICC Transformation Order and FNPRM, 26 FCC Rcd at 17744, para. 218. Although the
methodology determines capped values only for rate-of-return cost companies, the Commission directed
the National Exchange Carrier Association (NECA) to modify the HCLS formula for average schedule
companies to reflect the caps derived from the cost company data. See infra para. 10 and note 28.
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amount as calculated using the benchmarks. Instead, we will reduce support commencing in July
2012 by twenty-five percent of the difference between the support calculated using the study
area’s reported cost per loop and the support as limited by the benchmarks, unless that reduction
would exceed ten percent of the study area’s support as otherwise would be calculated based on
NECA cost data, absent implementation of this rule. Beginning January 1, 2013, we will reduce
support by fifty percent of the difference between the support calculated using the study area’s
reported cost per loop and the support as limited by the benchmarks in effect for 2013. Beginning
January 1, 2014, when we expect to have updated wire center boundaries, as discussed below, we
will update the regressions (the coefficients), and support will be limited, in full, by the
benchmarks in effect for 2014.13 When fully implemented, we estimate that the roughly 100
study areas that are capped would see approximately $65 million in support reductions, while the
roughly 500 study areas that are not capped would receive approximately $55 million in
additional support for broadband investment.

II.

BACKGROUND

6.
In the USF/ICC Transformation Order, the Commission adopted a framework to
establish reasonable limits on recovery of capital costs and operating expenses to improve the
incentives for rate-of-return carriers to invest prudently and operate efficiently.14 The
Commission explained that “under our [previous] rules, a company receives support when its
costs are relatively high compared to a national average – without regard to whether a lesser
amount would be sufficient to provide supported services to its customers. The [previous] rules
fail to create incentives to reduce expenditures; indeed, because of the operation of the overall cap
on HCLS, carriers that take prudent measures to cut cost under our [previous] rules may actually
lose HCLS support [sic] to carriers that significantly increase their costs in a given year.”15
7.
The Commission’s new rule places “limits on the HCLS provided to carriers
whose costs are significantly higher than other companies that are similarly situated” and
provides that “support will be redistributed to those carriers whose unseparated loop cost is not
limited by operation of the benchmark methodology.”16 The Commission found that its “new rule
will discourage companies from over-spending relative to their peers” and “provide additional
support to those companies that are otherwise at risk of losing HCLS altogether, and would not
otherwise be well-positioned to further advance broadband deployment.”17
8.
The Commission set forth the parameters of the methodology the Bureau must


13 The Commission directed the Bureau annually to update the regressions. See USF/ICC Transformation
Order and FNPRM
, 26 FCC Rcd at 17744, para. 218. NECA, OPASTCO, and WTA sought
reconsideration on this point. Petition for Reconsideration and Clarification of the National Exchange
Carrier Association, Inc.; Organization for the Promotion and Advancement of Small Telecommunications
Companies; and Western Telecommunications Alliance, WC Docket No. 10-90, et al., at 10 (filed Dec. 29,
2011). This issue, and other arguments raised in petitions for reconsideration of the requirements adopted
in the USF/ICC Transformation Order and FNPRM, will be addressed at a future date by the full
Commission.
14 See USF/ICC Transformation Order and FNPRM, 26 FCC Rcd at 17744-45, para. 219.
15 Id.
16 Id. at 17745, para. 220.
17 Id.
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use to limit payments from HCLS.18 The Commission required the Bureau to compare
companies’ costs to those of similarly situated companies; concluded that statistical techniques
should be used to determine which companies shall be deemed similarly situated; provided a non-
exhaustive list of variables that the Bureau may consider for purposes of this analysis;19 granted
the Bureau discretion to determine whether other variables, such as soil type, would improve the
regression analysis; and sought comment in the USF/ICC Transformation FNPRM on sources of
publicly available soil data.20 The Commission delegated to the Bureau the authority to adopt and
implement a methodology within these parameters and to update the methodology as the Bureau
gains more experience and additional information.21
9.
The methodology proposed in Appendix H to the USF/ICC Transformation
FNPRM used quantile regression analyses, NECA cost data, and 2010 Census data to generate a
set of limits for each rate-of-return cost company study area.22 The proposal would have limited
the values used in eleven of the twenty-six steps in NECA’s Cost Company Loop Cost
Algorithm, which is used to calculate the study area’s total unseparated cost per loop, and
ultimately its HCLS. The proposed regression-derived limits were set at the 90th percentile of
costs for each of the eleven algorithm steps, compared to similarly situated companies for each
individual step. A company whose actual costs for a particular algorithm step are above the 90th
percentile would be limited to recovering amounts that correspond to the 90th percentile of cost;
i.e., the lesser of the company’s capped algorithm value and the actual value would be inserted
into the appropriate algorithm step for purposes of calculating the cost per loop used to determine
HCLS. The Commission sought comment on whether the 90th percentile is the appropriate
dividing line to disallow recovery of cost, or whether a lower or higher threshold, such as the 85th
percentile or the 95th percentile, would be more appropriate.23

III.

DISCUSSION

10.
In this order, we implement the Commission’s rule to use benchmarks to impose
reasonable limits on reimbursable capital and operating costs for rate-of-return carriers for
purposes of determining HCLS and adopt the methodology that the Bureau will use to determine
carrier-specific benchmarks for rate-of-return cost companies. Consistent with parameters set
forth by the Commission, we compare companies’ costs to those of similarly situated companies
using statistical techniques to determine which companies shall be deemed similarly situated.24


18 See id. at 17744, para. 217.
19 See id. The variables identified by the Commission were: number of loops, number of housing units
(broken out by whether the housing units are in urbanized areas, urbanized clusters, and nonurban areas), as
well as geographic measures such as land area, water area, and the number of census blocks (all broken out
by urbanized areas, urbanized clusters, and nonurban areas).
20 See id. at 17744, para. 217, 18060, para. 1083.
21 See id. at 17744, para. 217.
22 See id. at 18059-60, para. 1080-82, 18285-94, App. H. Although the Commission found that quantile
regression is an appropriate technique to use in setting benchmarks for reimbursable investment and
expenses, it invited further comment on alternative statistical techniques. Id. at 18060, para. 1082.
23 See id. at 18059-60, para. 1080.
24 These statistical techniques rely on a set of independent variables that control for a company’s costs
based on its situation, such as the population density and soil type of the area it serves. Section III.C below
describes the full set of independent variables we are adopting, which is expanded from the proposal in the
USF/ICC Transformation Order and FNPRM in response to the record we received.
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As described in more detail in the attached technical appendix, we use NECA cost data and
quantile regression analyses to generate a capital expense (capex) limit and an operating expense
(opex) limit for each rate-of-return cost company study area.25 The regression-derived limits are
set at the 90th percentile of costs for capex and opex compared to similarly situated companies.26
The capped values will be used in NECA’s loop cost algorithm in place of an individual
company’s actual cost data for those rate-of-return cost companies whose costs exceed the caps,
which will result in reduced support amounts for these carriers.27 As directed by the Commission,
NECA will modify the HCLS formula for average schedule companies to reflect the caps derived
from the cost company data.28 After application of the benchmark methodology, HCLS will be
recalculated to account for the additional support available under the overall cap on total HCLS.
Additional support will be redistributed to carriers whose loop cost is not limited by the
benchmark methodology, and those carriers are required to use the additional support to preserve
and advance the availability of modern networks capable of delivering broadband and voice
telephony service.29


25 See National Exchange Carrier Assoc., Inc., Universal Service Fund Data, NECA’s Study Results, 2010
Report (NECA 2010 USF Data), http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-
State_Link/Monitor/usf11r10.zip, available at http://transition.fcc.gov/wcb/iatd/neca.html. We use the
NECA data because the Commission determined that the benefits of using data it already collects on a
regular basis outweigh any advantages of an alternative approach. See USF/ICC Transformation Order
and FNPRM
, 26 FCC Rcd at 17746, para. 224.
When the Commission proposed to establish benchmarks for reimbursable capital and operating costs in
February 2011, its proposal was “based significantly on analysis submitted by the Nebraska Rural
Independent Companies.” Connect America Fund; A National Broadband Plan for Our Future;
Establishing Just and Reasonable Rates for Local Exchange Carriers; High-Cost Universal Service
Support; Developing a Unified Intercarrier Compensation Regime; Federal-State Joint Board on Universal
Service; Lifeline and Link-Up
; WC Docket Nos. 10-90, 07-135, 05-337, 03-109, CC Docket Nos. 01-92,
96-45, GN Docket No. 09-51, Notice of Proposed Rulemaking and Further Notice of Proposed
Rulemaking, 26 FCC Rcd 4554, 4624, para. 201 (2011) (footnote omitted) (USF/ICC Transformation
NRPM/FNPRM
). NRIC had submitted an analysis of capital expenditures and subsequently submitted an
analysis of operating expenses.
26 Specifically, the 90th percentile of costs compared to similarly situated peers means that, based on data
from all the carriers in the analysis, if there were 100 study areas with independent variable values, as
adopted in section III.C below, that were the same as those for the study area in question, 90 of them would
be expected to have capex and opex costs equal to or less than the 90th percentile prediction.
27 NECA’s HCLS formula, i.e., the 26-step Cost Company Loop Cost Algorithm, is available at
http://transition.fcc.gov/wcb/iatd/neca.html. See National Exchange Carrier Assoc., Inc., NECA’s
Overview of Universal Service Fund, Submission of 2010 Study Results, App. B (filed Sept. 30, 2011)
(NECA 2010 USF Overview), http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-
State_Link/Monitor/usf11af.zip.
28 USF/ICC Transformation Order and FNPRM, 26 FCC Rcd at 17744, para. 218. Specifically, we direct
NECA to file proposed modifications to the average schedule formula within 30 days of the release of this
order.
29 Beginning January 1, 2014, carriers unaffected by the benchmark limits will receive additional
redistributed support as calculated using a lower adjusted national average cost per loop (NACPL). The
lower NACPL will be the NACPL that would be used if total reduced support, as a result of the application
of the benchmark methodology, is redistributed to all carriers. Support to carriers affected by the
benchmark will be calculated using the NACPL established pursuant to section 36.622 of the
Commission’s rules. 47 C.F.R. § 36.622. During the transition periods July 1, 2012 to December 31, 2012
and January 1, 2013 to December 31, 2013, the total amount of HCLS available to study areas not affected
(continued....)
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11.
The methodology that we adopt builds on the proposed methodology in
Appendix H of the USF/ICC Transformation Order and FNPRM,30 but includes some significant
improvements based on the many useful comments and ex parte presentations in this proceeding,
the comments of two peer reviewers, and further analysis by the Bureau. As in the proposed
methodology, we use quantile regression analysis and NECA cost data to generate a set of limits
for each rate-of-return cost company study area and use the regression-derived limits in NECA’s
formula for calculating loop cost. We modify the proposal, however, by reducing the overall
number of regressions from eleven to two: one for capital expenditures and one for operating
expenditures. In addition, Commission staff examined and tested additional independent
variables that were available from publicly available data sources, placed additional data sources
in the record, and updated the methodology to reflect this further analysis. Below, and in the
attached technical appendix, we explain these changes to the proposed methodology and respond
to other significant issues raised in the record.

A.

Number of Regressions

12.
The most significant change in methodology is that this analysis generates two
caps for each company – a capex limit and an opex limit. The methodology proposed in the
FNPRM generated eleven different caps for each company that would have limited the values in
eleven of the twenty-six steps in NECA’s loop cost algorithm. Based on our review of the record
and further analysis, we conclude that a better approach is to divide a company’s total cost in step
twenty-five of the algorithm into its capex and opex components and use two regressions instead
of using eleven independent regressions.
13.
Commenters took differing views on the appropriate number of regressions.
Commenters supporting more aggregation argue that limiting total cost, or separately limiting
capital and operating expenses, is a better approach and suggest we use a single regression
equation, or at most two equations.31 One peer reviewer also recommended this approach.32


(...continued from previous page)
by the benchmark methodology will be the capped HCLS, as calculated pursuant to section 36.603(a) of the
Commission’s rules, less the total amount to be paid to study areas affected by the benchmark methodology
during the transition periods. HCLS paid to the study areas not affected by the benchmark methodology
will be calculated using an adjusted NACPL to produce the capped support pursuant to section 36.603(a) of
the Commission’s rules. 47 C.F.R. § 36.603(a). See infra section III.G.
We direct NECA to provide to the Bureau a recalculated NACPL for redistribution and a schedule of
HCLS for all carriers for the six-month period of July 1, 2012 to December 31, 2012 within 30 days of the
release of this order. Consistent with current practice, the filing NECA makes each October with the
Commission shall include NACPL information and the schedule of HCLS for all carriers for the next year.
30 USF/ICC Transformation Order and FNPRM, 26 FCC at 18059-62, paras. 1079-88, 18285-94, App. H.
31 See, e.g., National Association of State Utility Consumer Advocates (NASUCA) et al. Comments, WC
Docket No. 10-90 et al., at 52 (filed Jan. 18, 2012) (NASUCA et al. Comments) (“To avoid the issue of
adopting an uneconomical set of inputs, the Commission could estimate only one equation, a total cost
equation.”); National Exchange Carrier Association et al. Comments, WC Docket No. 10-90 et al., at App.
E, 1 (filed Jan. 18, 2012) (Roger Koenker, “Assessment of Quantile Regression Methods for Estimation of
Reimbursable Cost Limits”) (Rural Association Comments) (“A preferable, and simpler, approach would
be to develop one conditional quantile model for aggregate costs.”); Nebraska Rural Independent
Companies (NRIC) Comments, WC Docket No. 10-90 et al., at 58 (filed Jan. 18, 2012) (NRIC Comments)
(“Consolidating the 11 caps into two caps will also improve the reliability of the associated regression
studies.”); NRIC Reply Comments, WC Docket No. 10-90 et al., at 6 (filed Feb. 17, 2012) (NRIC Reply
Comments) (agreeing with Koenker that “a single cost cap can work as well as or better than the two caps
(continued....)
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Conversely, some commenters argued that the proposed eleven limits would not have allowed the
algorithm to calculate support as it was intended,33 and proposed that costs be further
disaggregated to the underlying cost elements, i.e., “data lines,” that make up each algorithm
step.34
14.
The choice of how many cost limits to adopt reflects a balancing of
considerations. Using a greater number of regressions makes it possible to identify outliers at a
granular level, but fails to account for the interrelationships within the cost categories that feed
into the twenty-six step algorithm as identified in the record and in the peer review.35 In contrast,
using fewer regressions limits the Commission’s ability to identify outliers, but enables carriers to
account for the needs of individual networks and recognizes the fact that carriers may have higher
costs in one category that may be offset by lower costs in others. 36


(...continued from previous page)
NRIC originally suggested”); Carriers for Progress in Rural America Reply Comments, WC Docket No.
10-90 et al., at 12 (filed Feb. 17, 2012) (proposing “that the Commission’s model be redesigned to
maximize carriers’ overall efficiency,” [which] “could be accomplished by reducing the eleven cost
categories to just two categories: a limit on capex and a limit on opex.”).
32 Sanyal Peer Review at 1 (“By disaggregating the total cost function, and estimating the cost lines
separately using quantile regression, and then adding them up, assumes that the quantile of the sums equals
the sum of the quantiles. An argument that is similar to the sum of means of a random variable being equal
to the mean of the sum. However, this relationship does not hold true for quantile regressions.”).
33 See, e.g., Moss Adams et al. Comments, WC Docket No. 10-90 et al., at 16 (filed Jan. 18, 2012) (Moss
Adams et al. Comments ) (arguing that the proposed methodology does not allow NECA’s formula for
calculating loop cost to calculate support as it was intended because the benchmarks limit algorithm steps
in the formula rather than the data lines); Chillicothe Telephone Company Comments, WC Docket No. 10-
90 et al., at 7 (filed Jan. 18, 2012) (Chillicothe Comments); Central Texas Comments, WC Docket No. 10-
90 et al., at 8- 9, 10 (filed Jan. 18, 2012) (Central Texas Comments). NECA collects cost data from rate-of-
return cost companies and the data lines for investments and expenses generally correspond to specific Part
32 accounts or subaccounts. See NECA 2010 USF Overview,
http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-State_Link/Monitor/usf11af.zip, App. A
(Universal Service Fund: 2011 Data Collection Instructions) available at
http://transition.fcc.gov/wcb/iatd/neca.html.
34 See Moss Adams et al. Comments at 16 (noting that “all of the algorithm lines are calculations based on
various data lines, so any proposed limitations can also be accomplished by adjusting the data lines”).
Although some parties recommend placing limits only on certain cost categories, see, e.g., Accipiter
Comments, WC Docket No. 10-90 et al., at 19 (filed Jan. 18, 2012) (Accipiter Comments), using data lines
would inevitably increase the number of separate regressions.
35 See, e.g., NRIC Comments, at 12, 55-59; NASUCA et al. Comments at 50 (arguing that the unintended
consequences of the proposed methodology would include “large payments to accountants to develop
techniques that allow carriers to avoid the constraints and the incentive to adopt an uneconomical set of
inputs”); Sanyal Peer Review at 2 (“[I]ndividual cost capping ignores any complementary or
substitutability between the various cost components.”).
36 See, e.g., Rural Association Comments at App. D, 14 (“By limiting each account separately, without
regard to needs of individual networks, the Commission’s method discourages network optimization.”);
NASUCA et al. Comments at 51 (arguing that under the proposed methodology “the carrier has an
incentive to choose those inputs that allow it to remain under all of the caps, even though a different set of
inputs would lead to a lower cost of service, because when the carriers adopts the lower total cost of service
inputs it may exceed the cap related to just one of the inputs”); Accipiter Comments at 18 (“[T]he
individual cost caps should consider the interplay between different cost categories to avoid penalizing a
(continued....)
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15.
Balancing these considerations, we conclude that it is appropriate to reduce the
number of separate cost caps set from the proposed approach in Appendix H, but to retain
separate limits for capex and opex. We are persuaded that limiting eleven separate cost
categories could have the effect of overly limiting carriers’ ability to optimize among spending
tradeoffs. At the same time, an approach that only limited total cost would provide fewer
safeguards against overspending. Capital and operating expenditures reflect fundamentally
different measures of business performance. Using two regressions instead of one provides
carriers flexibility to manage their operations, while still enabling the Commission to identify
more instances where carriers spend markedly more in either category than their similarly-
situated peers.
16.
The approach we adopt is also supported by other considerations. In particular,
the methodology we adopt simplifies the process of fitting the benchmark computation within the
structure of NECA’s loop cost algorithm.37 Instead of potentially limiting values in eleven of the
twenty-six steps, we only change the value for companies that exceed the caps in step twenty-
five, total unseparated costs.38 Although we divide the components of step twenty-five into capex
and opex components for purposes of running two regressions and create separate capex and opex
limits, the two components are added together for purposes of calculating total costs, study area
cost per loop, and ultimately HCLS.39

B.

Defining Capex and Opex

17.
As discussed below and in more detail in the technical appendix, we define capex
as the plant-related costs in step twenty-five, which include return on capital and depreciation,
and define opex as the remaining components that are added in step twenty-five to calculate total
costs.40 These revised definitions of capex and opex differ from those used in the proposed
methodology in several important ways.
18.
The most important revision to the capex definition is the treatment of
depreciation in relationship to capital costs. To determine capex limits, the proposed
methodology created separate caps for two categories of gross plant (cable and wire facilities, and
central office equipment), and for the depreciation and amortization associated with those plant
categories.41 In the revised methodology, we define capex as the return on net plant and


(...continued from previous page)
higher investment in one cost category to produce lower costs in another category.”). Accipiter also argues
that we should select fewer individual cost categories subject to limits and only limit cost categories where
incentives to overspend may exist. See Accipiter Comments at 19.
37 It is important that the methodology fit within this framework because the Commission modified the
HCLS mechanism; it did not replace it with a new regime.
38 Step twenty-five is the sum of steps thirteen through twenty-four.
39 For companies whose actual capex and/or opex exceed the benchmarks, the capped values will be added
in step twenty-five in place of an individual company’s actual cost data. Capex components will be
summed into step 25A and opex into step 25B; step 25C becomes the new total unseparated costs. See
Appendix A at para. 6.
40 As discussed in the technical appendix, for the dependent variables, the regressions use the natural log of
the capex components and the natural log of the opex components. See infra Appendix A at paras. 11, 23.
41 The proposed methodology created separate caps for steps 1, 2, 17 and 18 of the NECA algorithm. See
USF/ICC Transformation Order and FNPRM
, 26 FCC at 18288, App. H, para. 15.
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depreciation.42 Many commenters pointed out that the proposed methodology did not properly
account for accumulated depreciation and depreciation expense, and we agree.43 We do not
agree, however, with those who argue that depreciation expense should not be included in the
regression analysis.44 Although depreciation is termed an “expense” for regulatory accounting
purposes, as the Rural Associations and several other commenters point out, depreciation expense
is properly considered as a component of capital costs because it is directly related and calculated
as a result of capital investment.45 The proposed methodology would have limited gross plant,
but did not adjust the accumulated depreciation or depreciation expense as would have been
necessary when gross plant was limited by the benchmark. The method we now adopt includes
net plant rather than gross plant, so we appropriately account for accumulated depreciation.46
19.
Our revised opex definition includes the remaining components that are summed
in step 25 in the NECA algorithm to determine total unseparated costs.47 The proposed
methodology excluded three of these – corporate operations expense, operating taxes, and rents –
which we now include in determining opex. In the USF/ICC Transformation Order, the
Commission revised the formula for limiting recovery of corporate operations expenses for HCLS
in section 36.621(a)(4) of the Commission’s rules.48 Because of this separate limitation, the
proposed methodology did not create an additional limit for corporate operations expense. Now


42 Capex includes the return component for cable and wire facilities category 1 (C&WF) (step 23); the
return component for central office equipment category 4.13 (COE) (step 24); depreciation and
amortization expense assigned to C&WF (step 17); and depreciation and amortization expense assigned to
depreciation assigned to COE (step 18).
43 See, e.g., Moss Adams et al. Comments at 15-18; Rural Association Comments at 67-68, App. D at 9-11;
Chillicothe Comments at 6-9; Central Texas Comments at 8- 9, 14-16.
44 Some commenters argue that regression should not be used to limit depreciation expense, but suggest an
alternative method of limiting depreciation. See, e.g., Moss Adams et al. Comments at 18 (recommending
that “regression not be used to limit depreciation expense,” but arguing that “depreciation expense
limitations should be computed as the percentage of the limitation of the associated plant investment
multiplied by the depreciation expense”); Chillicothe Comments at 9; Central Texas Comments at 14;
Guadalupe Valley Telephone Cooperative Comments, WC Docket No. 10-90 et al., at 5-6 (filed Jan. 18,
2012) (Guadalupe Valley Comments). Another commenter argues that there is no need to limit
depreciation expense at all. See NRIC Comments at 59 (“Since depreciation rates are regulated, and
investment itself is capped, there is no need to cap depreciation expense.”).
45 See, e.g., Moss Adams et al. Comments at 18; Rural Associations Reply Comments, App. B at 3; Letter
from Michael R. Romano, NTCA, to Marlene H. Dortch, FCC, WC Docket No. 10-90 et al., at 2 (dated
March 23, 2012).
46 Instead of creating separate caps for step 1 (C&WF) and step 2 (COE), the revised methodology includes
the return on net plant steps 23 and 24 in the capex regression. The return component for CW&F is
calculated in step 23 by adding CW&F in step 1 to CW&F materials and supplies in step 7, subtracting
accumulated depreciation assigned to CW&F in step 9, and multiplying that value by the 11.25%
authorized rate of return to determine the return component for C&WF. The return component for COE in
step 24 is calculated in a similar manner. The revised methodology recognizes that materials and supplies
are plant-related capital costs and a component of the return on capital in steps 23 and 24.
47 Opex includes C&WF maintenance (step13), and COE maintenance (step 14); network expenses (steps
15 and 16); corporate operations expense (step 19); operating taxes (step 20); corporate benefits (step 21),
and rents (step 22).
48 See 47 C.F.R. § 36.621(a)(4); USF/ICC Transformation Order and FNPRM, 26 FCC at 17747-49, paras.
227-33. The Commission also extended the corporate operations limitation to interstate common line
support (ICLS). Id.
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that we are analyzing all operating costs as a whole, it is appropriate to include corporate
operations expense, as well as the other operating expenses, taxes and rents.49 For purposes of
this analysis, we will use either a carrier’s actual corporate operations expense or the amount
allowable under section 36.621(a)(4), whichever is less. By using the allowable amount, we
avoid restricting carriers affected by section 36.621(a)(4) twice for their corporate operations
expenses above that limitation.50

C.

Selection of Independent Variables

20.
The revised methodology also includes additional independent variables that
were suggested by commenters and one of the peer reviewers, and eliminates some that had been
included in the methodology proposed in the USF/ICC Transformation FNPRM, because we
found the new variables to be better estimators of cost. In the USF/ICC Transformation FNPRM,
the Commission noted that NRIC’s Capital Expenditure Study included variables for frost index,
wetlands percentage, soils texture, and road intersections frequency, and invited commenters
advocating the inclusion of additional independent variables to identify the data source,
completeness, and cost of the additional data, if not publicly available.51 The Commission
specifically sought comment on sources of soil data other than the Soil Survey Geographic
Database (SSURGO) used in the NRIC study and how to deal with areas where the SSURGO
data are missing or incomplete.52 Many commenters suggest additional variables, and Bureau
staff examined those for which data were available. The technical appendix describes in more
detail the independent variables included in the methodology, those examined but excluded, and
those that commenters suggested but that could not be included because the data were either
unavailable to the Commission, nonpublic, or could not be generated at the study area level.53
We briefly discuss the variables included in the revised methodology below.
21.
The methodology uses cost-driving variables directly where available and
proxies that are sufficiently correlated with cost drivers where necessary. For example, the
number of loops is a direct measure of a study area’s scale, and the number of road miles is a
proxy for total loop length.54 Because most cable follows roads, it is reasonable to believe that
the number of road miles in a study area is a good proxy for the cabling required to serve that


49 For further discussion, see Appendix A at paras. 23, 26-28.
50 Most study areas are not affected by the corporate operations expense limitation in section 36.621(a)(4).
NRIC argues that, if there were a single cap on total costs, there would be no need to cap a single expense,
if total costs remain reasonable. See NRIC Reply Comments at 7-8. As an alternative to eliminating the
corporate operations expense limitation, NRIC recommends the approach we take here. See NRIC Reply
Comments at 8 n.17. (“Alternatively, even if the Commission decided to retain some kind of separate
corporate operations cap, it could still constrain factor AL19, which is corporate operations expense, and
the result would flow through automatically into the overall cap calculation for AL26.”).
51 See USF/ICC Transformation Order and FNPRM, 26 FCC at 18060-61, para. 1083.
52 See id.; U.S. Department of Agriculture, Natural Resources Conservation Service, Available Soil Survey
Data (SSURGO) (2012), available at http://soildatamart.nrcs.usda.gov/ (last visited Apr. 24, 2012).
53 As discussed in the technical appendix, the regressions use the natural logs of the independent variables
except those that are dummy variables, a pure index, or a percentage. See infra Appendix A at para. 11.
54 See infra Appendix A at para. 33. Several commenters argue that some measure of loop length is an
important cost driver and suggest that some carriers already provide average loop lengths and other relevant
data to the Rural Utilities Service (RUS). See, e.g., Central Texas Comments at 6-7; Chillicothe Comments
at 3-4; Accipiter at 26; Moss Adams et al. Comments at 11-12.
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area.55 Some commenters suggest that the age of plant is an important variable, and we agree.56
Many carriers have recently replaced aging plant with modern communications networks capable
of providing voice and broadband service, and those carriers are not similarly situated to carriers
with plant that is more fully depreciated. Accordingly, while data on the average age of plant are
not readily available, the revised methodology now includes a variable for the percentage of plant
that has not yet been depreciated, which is highly correlated with plant age. The revised
methodology also includes variables that account for customer dispersion: density (housing units
divided by square miles); number of exchanges, which roughly accounts for the population
centers in a study area; and portion of households in urbanized clusters or urbanized areas.57
22.
In addition, the revised methodology includes several geographic independent
variables that Bureau staff developed from various data sources. First, we agree with the many
commenters who argue that the proposed methodology should include soils data.58 Bureau staff
used the U.S. General Soil Map (STATSGO2) soils database to construct two soil-based variables
that are included in the revised methodology: depth of bedrock, and soils difficulty.59 Although
the SSURGO database contains a richer set of soil variables and data at a more granular level
than STATSGO2, it does not provide data for the entire country. Some commenters argue that
we should use the SSURGO data where available and STATSGO2 for the remaining study areas,
but we decline to use an approach that treats study areas differently depending on the availability
of the data.60 In addition, NRIC’s Capital Expenditure Study includes a frost index developed
from the SSURGO data, but this information is not available for all areas in the STATSGO2
database. Several commenters discuss the need for such a frost index.61 As a proxy for this
information, Bureau staff developed a climate variable based on the average annual minimum


55 Other proxies for scale used in the methodology are the number of road crossings and the number of
commonly-owned study areas in a state. In its Capital Expenditure Study, NRIC predicted that road
intersections would slow construction and impose other costs, and Bureau staff concludes this is another
good proxy for scale. See NRIC Capital Expenditure Study at 10. In addition, Bureau staff expects that the
number of commonly-owned study areas would be a good predictor of costs because some expenses could
be shared among study areas. See infra Appendix at paras. 35, 37.
56 See, e.g., Accipiter Comments at 5-6, 33-34; Guadalupe Valley Comments at 3-4; Carriers for Progress
in Rural America Comments, WC Docket No. 10-90 et al., at 6-7 (filed Jan. 18, 2012); infra Appendix A at
para. 38.
57 See infra Appendix A at para. 39-41.
58 See, e.g., NRIC Comments at 22-24; Moss Adams Comments et al. at 8; ATC Communications
Comments, WC Docket No. 10-90 et al., at 3 (filed Jan. 18, 2012); Chillicothe Comments at 2; Northern
Telephone Cooperative Comments, WC Docket No. 10-90 et al., at 3 (filed Jan. 18, 2012) (Northern
Telephone Comments); Washington Independent Telecommunications Association et al. Comments, WC
Docket No. 10-90 et al., at 4-5 (filed Jan. 17, 2012).
59 See Appendix A at paras. 43,45; U.S. Department of Agriculture, Natural Resources Conservation
Service, U.S. General Soil Map (STATSGO2) available at http://soils.usda.gov/survey/geography/statsgo
(last visited Apr. 24, 2012).
60 See NRIC Comments at 24; NASUCA et al. Comments at 46; infra Appendix at paras. 53-54.
61 See, e.g., Blooston Rural Broadband Carriers Comments, WC Docket No. 10-90 et al., at 2 (filed Jan. 18,
2012) (Blooston Comments); Interbel Comments, WC Docket No. 10-90 et al., at 10 (filed Jan. 18, 2012)
(Interbel Comments); NRIC Comments at 25.
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temperature from the U.S. Department of Agriculture’s hardiness index.62
23.
We also agree with commenters who emphasized that carriers serving particular
areas such as Alaska, Tribal lands, and national parks could face unique challenges. In particular,
some commenters suggest that it is more costly to provide service on Tribal lands;63 the
methodology now includes an additional independent variable for the percentage of each study
area that is a federally-recognized Tribal land.64 In addition, Alaskan commenters argued that
Alaska is unique because of its harsh climate and other factors; accordingly, the methodology
now includes a variable indicating whether or not the study area is in Alaska.65 Some
commenters also argued that it is more difficult to construct and maintain networks in national
parks;66 the methodology also now includes an additional independent variable for the percentage
of each study area that lies within a national park.67 NRIC’s Operating Expenses Study found
that operating expenses were correlated with regions, and Bureau staff tested variables for the
four census-based regions: Western, Midwest, Northeast and South.68 The revised methodology
also includes the two that were significant: the Midwest and Northeast.

D.

Use of Boundary Data

24.
All geographic independent variables were rolled up to the study area using Tele
Atlas wire center data, which is a widely-used commercially available comprehensive source for
this information.69 Several commenters question the accuracy of those boundaries.70 For
example, the Rural Associations point to a NECA study that concluded many of the Tele Atlas


62 See infra Appendix A at para. 47; see also U.S. Department of Agriculture, U.S. National Arboretum,
Plant Hardiness Zone Map (2012), available at http://www.usna.usda.gov/Hardzone (last visited Apr. 24,
2012).
63 See, e.g., Gila River Telecommunications Comments, WC Docket No. 10-90 et al. (filed Jan. 18, 2012);
Hopi Telecommunications Comments, WC Docket No. 10-90 et al. (filed Jan. 18, 2012); Mescalero
Apache Telecom Comments, WC Docket No. 10-90 et al. (filed Jan. 18, 2012); National Tribal
Telecommunications Association Comments, WC Docket No. 10-90 et al. (filed Jan. 18, 2012); Sacred
Wind Comments, WC Docket No. 10-90 et al. (filed Jan. 17, 2012); Alexicon Telecommunications
Consulting Comments, WC Docket No. 10-90 et al., at 18-19, App. B (filed Jan. 18, 2012) (Alexicon
Comments).
64 See infra Appendix at para. 49-50.
65 See, e.g., Alaska Rural Coalition Comments, WC Docket No. 10-90 et al., at 17-19 (filed Jan. 18, 2012);
Copper Valley Telephone Cooperative Comments, WC Docket No. 10-90 et al., at 5-7 (filed Jan. 17, 2012).
66 See, e.g., Interbel Comments at 3.
67 See infra Appendix at para. 49-50. In the future, if sufficient data become available, we may consider
including a variable that would account for all federal lands (i.e., that is not limited to national park lands).
68 See NRIC Operating Expense Study at 8; infra Appendix at para. 52.
69 TomTom Telecommunications Suite 2011.09 (formerly Tele Atlas North America), Wire Center
Premium, for wire center boundary and central office location information. Earlier study area boundary
versions were also used to exclude the portions of study areas that were associated with frozen support.
70 See, e.g., Calaveras Telephone Comments, WC Docket No. 10-90 et al., at 6-7(filed Jan. 18, 2012); Eagle
Telephone Comments, WC Docket No. 10-90 et al., at 3 (filed Jan. 18, 2012); Moss Adams et al.
Comments at 10; Northern Telephone Comments at 2-3; NRIC Comments at 2-29.
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boundaries “differ quite significantly from actual boundaries.”71 In addition, some companies
that argue that their boundaries, and in particular the resulting measure of square miles in their
service territories, were inaccurate in the proposed methodology have asked how they could
correct errors in the data.72
25.
The only comprehensive set of wire center boundaries are those commercially
available from companies such as Tele Atlas and GeoResults. There is precedent for using Tele
Atlas’ (or a predecessor company’s) boundaries. In particular, the Commission’s hybrid cost
proxy model uses a customer location data set that was created using an earlier version of the
Tele Atlas boundaries.73
26.
We decline to adopt NRIC’s proposal that we modify study area boundaries
before implementing the regression methodology based on publicly available state maps.74 While
many states have study area maps available on-line,75 the vast majority of those maps will not
allow Commission staff to calculate the information required for the analysis we adopt. Variables
like road miles and those related to local soil conditions require having GIS-based boundaries that
can be overlaid with other GIS-based data sets (like road networks and databases of soil
conditions). It is not practical to derive such information from printed maps, images on websites
or PDF files with any accuracy. In addition, it is not clear whether state maps represent
authoritative boundaries. Therefore, we do not believe that the proposal by NRIC is a practical
means to derive more reliable study area boundary information quickly.76
27.
Nevertheless, we recognize concerns remain regarding inaccuracies in this data
set, and we adopt a two-part process to address these concerns. First, in the near term, we will


71 Rural Association Comments, Appendix D at 3-4. (“Of 357 study areas for which NECA has actual
boundaries, 144 are not accurate within 5%, and 80 are not even accurate within 20%. A significant
number differ by more than 50%, and a few are completely (i.e., 100%) inaccurate.”). Id. See also Joint
Comments of NECA, NTCA, OPASTCO, WTA, and the Rural Alliance, WC Docket No. 10-90 et al., at
Attach. at 1-3 (filed July 12, 2010) (NECA et al. July 12, 2010 Comments).
72 See, e.g., Letter from Joshua Seidemann, NTCA, to Marlene Dortch, FCC, WC Docket No. 10-90 et al.
(filed Mar. 21, 2012).
73 Business Location Research was subsequently acquired by Geographic Data Technology, which was
acquired by Tele Atlas. See Federal-State Joint Board on Universal Service, Forward-Looking Mechanism
for High Cost Support for Non-Rural LECs
, CC Docket Nos. 96-45, 97-160, Tenth Report and Order, 14
FCC Rcd 20156, 20181, para. 51 (1999) (Tenth Report and Order), affirmed, Qwest Corp. v. FCC, 258
F.3d 1191 (10th Cir. 2001) (Qwest I). The Commission has also used the TeleAtlas boundaries to create
maps of study areas receiving the highest per-line support amounts and the states with the most competitive
eligible telecommunications carriers in response to requests from the U.S. House of Representatives,
Committee on Energy and Commerce. See, e.g., FCC Responses to Requests 5 and 7 (July 27, 2011),
available at http://democrats.energycommerce.house.gov/index.php?q=news/bipartisan-energy-and-
commerce-leaders-release-information-on-universal-service-fund.
74 See Letter from Cheryl L. Parrino, Parrino Strategic Consulting Group, to Marlene H. Dortch, FCC, WC
Docket No. 10-90 et al., Attach. A, at 4 (filed Apr. 13, 2012).
75 See id., Attach. B.
76 The Rural Associations acknowledge that compiling a new dataset of study area boundaries will require
substantial effort because “[v]erifiable studies of documented serving areas of all RLECs would need to be
completed to assure that calculations are correct. These studies would involve obtaining maps of study area
boundaries for each RLEC, which would need to be digitized to create a workable database of actual study
area boundaries.” Rural Association Comments, App. D at 4.
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provide a streamlined, expedited waiver process for carriers affected by the benchmarks to correct
any errors in their study area boundaries. Second, to correct any remaining inaccuracies in the
Tele Atlas data set, we will issue a Public Notice to initiate the process of collecting study area
boundaries directly from all rate-of-return carriers. The Public Notice will seek comment on data
specifications for a data request that the Bureau would issue after receiving input from the public
and interested parties. We expect that we will have updated boundary data before we rerun the
regression to calculate capex and opex limits that will be used for calculating support for 2014, at
which time the limits will apply in full.77
28.
In light of the protections we adopt to address errors in the TeleAtlas data, we
decline to delay implementation of the benchmarks beyond the 18-month phase-in described
below. The Commission anticipated that “HCLS benchmarks will be implemented for support
calculations beginning July 2012.”78 In many cases, more accurate boundaries would not change
whether or not a particular company is capped or not by the benchmark methodology. And the
streamlined, expedited waiver process we adopt to correct boundaries in the near-term will
address those specific instances where an inaccurate boundary could result in a company losing
more support than it would otherwise.79
29.
Specifically, any carrier whose actual boundaries are different from the
boundaries used by the Bureau in the methodology we adopt today may file a petition for waiver
in accordance with section 1.3 of the Commission’s rules.80 To enable the Bureau to determine
whether there are special circumstances (i.e., inaccurate boundaries) supporting a waiver,
petitioners must provide accurate boundary information in a manner and format that Bureau staff
can readily evaluate and process.81 In Appendix C, the Bureau sets forth a template for filing
study area maps to help potential petitioners file information efficiently, accurately, and in a
manner that will permit the Bureau to evaluate and process the information expeditiously.
30.
While potential petitioners may choose to submit boundary information in other
formats, the Bureau cautions that information submitted in other formats may require additional
processing, and that the processing could introduce errors and/or delay. For example, if
petitioners file hard copy maps, those would need to be rectified (stretched) to have a spatial


77 We emphasize that because we phase in the benchmarks, companies will experience no more than half of
the reduction otherwise required by the benchmarks until we have updated boundary data. Phasing in the
application of the limits over 18 months helps address concerns about the accuracy of the existing boundary
data in the interim period before the limits apply in full.
78 USF/ICC Transformation Order and FNPRM, 26 FCC at 17744, para 216.
79 Consistent with existing practice, if such a waiver request is granted and a true-up is required, a carrier’s
support amounts will be trued-up back to July 1, 2012.
80 Generally, the Commission’s rules may be waived if good cause is shown. 47 C.F.R. § 1.3. The
Commission may exercise its discretion to waive a rule where the particular facts make strict compliance
inconsistent with the public interest. Northeast Cellular Telephone Co. v. FCC, 897 F.2d 1164, 1166 (D.C.
Cir. 1990) (Northeast Cellular). In addition, the Commission may take into account considerations of
hardship, equity, or more effective implementation of overall policy on an individual basis. WAIT Radio v.
FCC
, 418 F.2d 1153, 1159 (D.C. Cir. 1969); Northeast Cellular, 897 F.2d at 1166. Waiver of the
Commission’s rules is appropriate only if both (i) special circumstances warrant a deviation from the
general rule, and (ii) such deviation will serve the public interest. NetworkIP, LLC v. FCC, 548 F.3d 116,
125-128 (D.C. Cir. 2008); Northeast Cellular, 897 F.2d at 1166.
81 See infra Appendix C.
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reference, and digitized by Bureau staff. Accordingly, petitioners that do not wish to use the
Bureau’s template may wish to consult with Bureau staff in advance of filing boundary
information in alternate formats to ensure that the information submitted can be processed
quickly.
31.
Regardless of how the boundary information is filed, an officer of the company
must certify under penalty of perjury that the information provided is accurate. We also
emphasize that carriers using this waiver process solely to seek changes to their study area
boundaries used in the benchmark methodology are not required to file the financial data and
other information required for waivers as set forth in the USF/ICC Transformation Order.82 The
financial data and other information set forth in the USF/ICC Transformation Order is relevant
for petitions for waiver alleging that “reductions in current support levels would threaten [a
carrier’s] financial viability, imperiling service to consumers in the areas they serve.”83 In
contrast, when considering whether there are special circumstances and the public interest is
served by granting a waiver of the benchmark methodology, we will be focusing on ensuring that
accurate data is used to perform the necessary computations, regardless of the extent of support
reduction. In addition, carriers using this streamlined, expedited waiver process to make
technical corrections to their study area boundaries need not pay the filing fee associated with
requests for waiver of Part 36 separations rules.84 With the safeguard provided by this
streamlined, expedited waiver process, we conclude it is appropriate to use the Tele Atlas
boundaries on an interim basis.

E.

Use of Quantile Regression and the 90th Percentile Cost Threshold

32.
As discussed in the technical appendix, we conclude that quantile regression
analysis is the appropriate methodology to use to identify study areas that have capex and opex
costs that are much higher than those of their similarly situated peers and to cap their cost
recovery at amounts that are no higher than the vast majority of similarly situated study areas.85
We also conclude that we should set the regression-derived limits at the 90th percentile of costs
for capex and opex compared to similarly situated companies.
33.
Some commenters criticized the use of the 90th percentile, arguing that it was
unreasonable because approximately forty percent of study areas in the methodology proposed in
the FNPRM would have been subject to limits in one or more of the eleven cost categories used
in that analysis.86 On further consideration, we have concluded that the proposed methodology
was over-inclusive because a carrier that exceeded the cap in only one category, but had costs
well below the caps in the other ten, would have received reduced support. As discussed above,
however, we are adopting a revised methodology that relies on aggregated capex and opex caps.
Applying the revised methodology with a 90th percentile cap limits reimbursable costs for only
fifteen percent of the study areas of cost companies. The net effect is fewer study areas will see
reduced support, and more companies will see additional support, due to the distribution of
support among HCLS recipients.


82 USF/ICC Transformation Order and FNPRM, 26 FCC at 17839-42, paras. 539-44.
83 Id. at 17839, para. 539.
84 See 47 C.F.R. § 1.1105.
85 See infra Appendix A at paras. 7-10.
86 See, e.g., Blooston Comments at 4; Rural Association Comments at 71
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34.
Accordingly, we do not agree with commenters who argue that we should limit at
most those carriers with costs above the 95th percentile.87 Indeed, we note that using the 90th
percentile with the modifications adopted today leads to approximately the same number of study
areas with capped costs as would have been the case if we were to use the 95th percentile with the
Appendix H methodology.88 We conclude that using the 90th percentile as part of the revised
methodology appropriately balances the Commission’s twin goals of providing better incentives
for carriers to invest prudently and operate more efficiently, and providing additional support to
further advance broadband deployment. By providing additional, redistributed HCLS to carriers
that do not have the highest costs among similarly situated companies, our budget for high-cost
support should enable more broadband deployment than if we continued funding more of the
highest cost companies at current levels.
35.
In view of the fact that many carriers will receive additional, redistributed HCLS,
we take this opportunity to emphasize the obligations that attach to the additional funding.
Section 254(e) of the Act requires that this additional funding – like all federal universal service
support – be used “only for the provision, maintenance, and upgrading of facilities and services
for which the support is intended.”89 Consistent with the USF/ICC Transformation Order, the
overarching intent is to preserve and advance the availability of modern networks capable of
delivering broadband and voice telephony service.90 Indeed, all rate-of-return carriers are
required to provide broadband upon reasonable request beginning July 1, 2012, as a condition of
receiving federal high-cost universal service support.91 Carriers must use their high-cost
universal service support – including any additional funding – in compliance with these
requirements.
36.
We further note that all rate-of-return carriers will be required to file a new build-
out plan, which accounts for the new broadband obligations, in 2013.92 Those plans must be
updated annually to reflect progress on network improvements and build-out, which should
reflect the impact of high-cost universal service support, including any additional funding.93 The
Commission will be reviewing those plans and updates, as well as other information provided in
the annual section 54.313 reports, to ensure that carriers are complying with their public interest
obligations, including their build-out requirements. Further, the progress report on those plans
will be part of the factual basis that supports the annual section 54.314 certification by the states
or carriers that support is being used for the intended purposes.94


87 See, e.g., Alexicon Comments at 14-15; NASUCA et al. at 53; NRIC Comments at 51-53.
88 Using the methodology proposed in Appendix H of the USF/ICC Transformation Order and FNPRM
and the 95th percentile would have limited reimbursable costs for approximately fifteen percent of the study
areas – no different than selecting the 90th percentile with the other improvements we adopt today.
89 47 U.S.C. § 254(e).
90 See USF/ICC Transformation Order, 26 FCC Rcd at 17670, para. 11, 17681, para. 51, para. 17854, para.
587.
91 See id. at 17740, para. 206.
92 See id. at 17854, para. 587.
93 See id.
94 See id. at 17859-61 paras. 607-612.
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F.

Other Issues

37.
Retroactivity. We disagree with commenters who assert that applying the
benchmarks to limit HCLS payments constitutes retroactive rulemaking.95 A rule does not
operate retroactively merely because it is “applied in a case arising from conduct antedating [its]
enactment” or “upsets expectations based on prior law.”96 Rather, a rule operates retroactively if
it “takes away or impairs vested rights acquired under existing law, or creates a new obligation,
imposes a new duty, or attaches a new disability in respect to transactions or considerations
already past.”97
38.
Here, it cannot fairly be said that the application of these benchmarks will take
away or impair a vested right, create a new obligation, impose a new duty, or attach a new
disability in respect to the carriers’ previous expenditures. There is no statutory provision or
Commission rule that provides companies with a vested right to continue to receive support at
particular levels or through the use of a particular methodology.98 Although application of the
benchmarks may affect the amount of support a carrier receives for expenditures made in 2010
(or before),99 it does not change the legal landscape in which those expenditures were made.
Rather, as the Commission observed in the USF/ICC Transformation Order, “section 254 directs
the Commission to provide support that is sufficient to achieve universal service goals, [but] that
obligation does not create any entitlement or expectation that ETCs will receive any particular
level of support or even any support at all.”100


95 See, e.g., GVNW Consulting Comments, WC Docket No. 10-90 et al., at 11-12 (filed Jan. 17, 2012) (“the
Commission’s proposal to adopt regression caps is unlawful and constitutes retroactive rulemaking”);
Alexicon Comments at 12-14 (“this result is substantially similar to retroactive ratemaking”); Blooston
Comments at 3-5 (“retroactive application of the [quantile regression analysis] . . . contravenes well-settled
principle [sic] of agency law and precedent”).
96 Landgraf v. USI Film Products, 511 U.S. 244, 269-70 (1994).
97 Marrie v. SEC, 374 F.3d 1196, 1207 (D.C. Cir. 2004) (quotation omitted); see also Blanco de Belbruno
v. Ashcroft
, 362 F.3d 272, 283 (4th Cir. 2004) (“to determine whether a regulatory change has retroactive
effect, we must determine that a rule ‘attaches new legal consequences to events completed before its
enactment’”) (quoting INS v. St. Cyr, 533 U.S. 289, 321 (2001)).
98 See USF/ICC Transformation Order, 26 FCC Rcd at 17770-71, para. 293; 47 U.S.C. § 254; see also
Rural Cellular Association v. FCC
, 588 F.3d 1095, 1103 (D.C. Cir. 2009) (“[the] purpose of universal
service is to benefit the customer, not the carrier”) (quotation omitted). We note that the Commission has
been seeking comment on whether and how to change the support methodology for rural carriers since
2004, which should have made it evident to those carriers that they are not guaranteed a particular level of
support. See Federal-State Joint Board on Universal Service, CC Docket No. 96-45, Order, 19 FCC Rcd
11538 (2004). Indeed, the Commission’s proposals to reform support for rural carriers have ranged from
the modest, targeted reforms adopted in the USF/ICC Transformation Order to more sweeping proposals to
auction all high-cost support. See, e.g., Federal-State Joint Board on Universal Service, WC Docket No.
05-337, CC Docket No. 96-45, Notice of Proposed Rulemaking, 23 FCC Rcd 2495 (2008).
99 See supra note 25.
100 USF/ICC Transformation Order, 26 FCC Rcd at 17745, para. 221; see also Members of the Peanut
Quota Holders Assoc. v. United States
, 421 F.3d 1323 (Fed. Cir. 2005), cert. denied, 548 U.S. 904 (2006)
(“The government is free to create programs that convey benefits in the form of property, but, unless the
statute itself or surrounding circumstances indicate that such conveyances are intended to be irrevocable,
the government does not forfeit its right to withdraw those benefits or qualify them as it chooses.”).
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39.
Indeed, consistent with the Commission’s focus on service to consumers, the
Commission declined to provide any group of companies with a blanket exception from universal
service reforms for past investments, recognizing that the current rules were not efficiently
serving universal service goals. Providing such exceptions would have made it impossible to
reform the system over any reasonable time period. Instead, the Commission established an
avenue for companies to demonstrate a need for temporary and/or partial relief from the new
rules to ensure its customers do not lose service.101 Moreover, our decision to phase in the
application of the limits over 18 months provides a greater opportunity for carriers to make any
necessary adjustments.
40.
Critically, the revised methodology now includes an independent variable that
captures age of plant, further addressing “retroactivity” concerns with respect to capex. Adding
this variable raises the cost limits for carriers that have invested recently, by allowing their costs
to be judged relative to a peer group of other carriers that have also invested recently. We also
note that application of the limits to operating expenses clearly presents no “retroactivity”
concerns.
41.
Predictability and Sufficiency. We also reject the argument that implementing
these benchmarks will undermine the predictability or sufficiency of support.102 At the outset, we
note that this general argument effectively seeks reconsideration of the Commission’s policy
judgment to adopt a rule imposing limits on capex and opex in the first instance, which is beyond
the scope of this order to implement a methodology as directed by the Commission. As the
Commission explained in the USF/ICC Transformation Order, the HCLS mechanism operates in
fundamentally the same way with or without the benchmarks.103 In both cases, a certain amount
of unpredictability exists because a carrier’s support depends in part on a national average that
changes from year to year, and companies “can only estimate whether their expenditures will be
reimbursed through HCLS.”104 Moreover, as the Commission has suggested, if anything, support
will now be more predictable for most carriers because the new rule discourages companies from
exhausting the fund by over-spending relative to their peers.105 The addition of several new
independent variables that capture attributes that do not change over time (e.g., depth of bedrock,
soils difficulty, the percentage of study area that is a federally-recognized Tribal land, the
percentage of each study area that lies within a national park, whether the study area is in the
Midwest, Northeast, or Alaska) also improves the predictability of support. In addition, as
described below, we will use the same regression coefficients for capex and opex in 2013 as those
calculated for 2012, which will provide more certainty as we phase in the application of the
limits. Accordingly, commenters’ concerns that support amounts will fluctuate radically from
year to year are speculative and unpersuasive.


101 USF/ICC Transformation Order, 26 FCC Rcd at 17745, para. 222; see also id. at 17839-42, paras. 539-
44.
102 See, e.g., Blue Valley Telecommunications Comments, WC Docket No. 10-90 et al., at 4-5 (filed Jan.
18, 2012); TCA Comments, WC Docket No. 10-90 et al., at 5-6 (filed Feb. 24, 2012); Rural Broadband
Alliance Reply Comments, WC Docket No. 10-90 et al., at 14-18 (filed Feb. 17, 2012); Letter from
Michael J. Balhoff, Balhoff & Williams, LLC, to Marlene H. Dortch, FCC, WC Docket No. 10-90 et al., at
Attach. at 8 (dated April 12, 2012).
103 USF/ICC Transformation Order, 26 FCC Rcd at 17745, para. 220.
104 Id.
105 See id.
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42.
As for sufficiency, the very purpose of the benchmarks is to ensure that carriers
as a whole receive a sufficient (but not excessive) amount of HCLS, which is one component of
high-cost support. As discussed above, the methodology compares carriers’ costs to those of
similarly situated carriers and reduces HCLS only to the extent that a carrier over-spends relative
to its peers. Moreover, excess support is redistributed to carriers that otherwise may be at risk of
losing HCLS altogether, and may not otherwise be well-positioned to further advance broadband
deployment. Thus, the application of benchmarks is not only consistent with the Commission’s
interpretation of “sufficient” as requiring that the fund remain “sustainable,” which the D.C.
Circuit found to be a reasonable interpretation in Rural Cellular Association v. FCC,106 but it also
complies with the stated intent of section 254 that the Commission’s universal service
mechanisms should preserve and advance universal service.107

G.

Implementation

43.
We will implement the limits on costs eligible for reimbursement though HCLS
beginning July 1, 2012, but we will not reduce support amounts immediately by the full amount
as calculated using the benchmarks. Instead, we will reduce support beginning July 1, 2012 and
until December 31, 2012 by twenty-five percent of the difference between the support calculated
using the study area’s cost per loop as reported by NECA and the support as limited by the
benchmarks, however, the reduction shall not be greater than ten percent of the study area’s
HCLS support based on the cost data filed with NECA. Beginning January 1, 2013 and until
December 31, 2013, we will reduce support by fifty percent of the difference between the support
calculated using the study area’s cost per loop as reported by NECA in October 2012 and the
support as limited by the benchmarks in effect for 2013. Beginning January 1, 2014, when we
expect to have updated wire center boundaries, as discussed above, we will update the regression
coefficients and incorporate the cost data submitted by NECA in October 2013, and support will
be limited, in full, by the benchmarks in effect for 2014.
44.
By delaying the full impact of the reductions until 2014, we provide companies
who would be adversely affected adequate time to make adjustments and, if necessary,
demonstrate that a waiver is warranted either to correct inaccurate boundary information and/or
“to ensure that consumers in the area continue to receive voice service.”108 For many companies
affected by the benchmarks, the initial twenty-five percent phase-in reduction is a small
percentage of their total HCLS. For those whose reduction would be more than ten percent of
their HCLS based on NECA cost data, we are limiting the reduction to ten percent for the
remainder of 2012. Moreover, continuing to limit the impact of support reductions in 2013
provides an additional opportunity for carriers to make further adjustments. On balance, we find
that this measured transition strikes a reasonable balance between the goals of promptly making
available additional support to those carriers who, under the new rule, will receive redistributed
HCLS to further advance broadband deployment and providing an adequate amount of time for
carriers that will experience reductions in support to make adjustments.


106 588 F.3d 1095, 1102-1103 (D.C. Cir. 2009).
107 See 47 U.S.C. § 254(b)(5).
108 USF/ICC Transformation Order and FNPRM, 26 FCC at 17839, para. 539.
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45.
We also take steps to provide more certainty regarding the operation of the limits
on capex and opex.109 In particular, to provide carriers with more certainty regarding the impact
of the fifty percent phase-in in 2013, we will use the same regression coefficients for capex and
opex in 2013 as those calculated for 2012, which enables carriers to estimate their 2013 support
now.110 That is, we will not update the regressions, but we will recalculate individual study area
caps based on the 2011 cost data filed with NECA, which will be submitted to the Commission in
NECA’s annual filing in October 2012. This will allow higher caps for those study areas with
significant network investment in 2011.111 By taking into account the 2011 cost data filed with
NECA, study areas that may not have qualified for HCLS based on their costs in prior years may
be eligible to qualify for HCLS in 2013, thereby providing those study areas with additional
support for broadband investment. In addition, study areas whose costs drop below their
computed benchmark for 2013 no longer will be considered capped, and therefore will receive
support based on their own actual costs and will be eligible to receive redistributed support like
other uncapped study areas.

IV.

PROCEDURAL MATTERS

A.

Paperwork Reduction Act

46.
This document does not contain new or modified information collection
requirements subject to the Paperwork Reduction Act of 1995 (PRA), Public Law 104-13. In
addition, therefore, it does not contain any new or modified information collection burden for
small business concerns with fewer than 25 employees, pursuant to the Small Business
Paperwork Relief Act of 2002, Public Law 107-198, see 44 U.S.C. 3506(c)(4).

B.

Final Regulatory Flexibility Act Certification

47.
Final Regulatory Flexibility Certification. The Regulatory Flexibility Act of
1980, as amended (RFA)112 requires that a regulatory flexibility analysis be prepared for
rulemaking proceedings, unless the agency certifies that "the rule will not have a significant
economic impact on a substantial number of small entities."113 The RFA generally defines "small
entity" as having the same meaning as the terms "small business," "small organization," and
"small governmental jurisdiction."114 In addition, the term "small business" has the same


109 NTCA, for example, expressed concern about “dynamic, year-by-year alteration of the caps.” See Letter
from Michael R. Romano, NTCA, to Marlene H. Dortch, FCC, WC Docket No. 10-90 et al., at 1-2 (filed
Mar. 23, 2012); Letter from Michael R. Romano, NTCA, to Marlene H. Dortch, FCC, WC Docket No. 10-
90 et al., at 1-2 (filed Apr. 2, 2012).
110 In addition, as discussed above, we add several new independent variables that capture attributes that do
not change over time thereby improving the predictability of support. See supra section III.C and para. 41.
111 This could allow higher caps for study areas with significant network investment in 2011; for example,
if that investment causes the percentage of undepreciated plant to grow.
112 The RFA, see 5 U.S.C. § 601 et seq., has been amended by the Contract With America Advancement
Act of 1996, Pub. L. No. 104-121, 110 Stat. 847 (1996) (CWAAA). Title II of the CWAAA is the Small
Business Regulatory Enforcement Fairness Act of 1996 (SBREFA).
113 5 U.S.C. § 605(b).
114 5 U.S.C. § 601(6).
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meaning as the term "small business concern" under the Small Business Act.115 A small business
concern is one which: (1) is independently owned and operated; (2) is not dominant in its field of
operation; and (3) satisfies any additional criteria established by the Small Business
Administration (SBA).116
48.
This Order implements, but does not otherwise modify, the rule adopted by the
Commission in USF/ICC Transformation Order.117 These clarifications do not create any
burdens, benefits, or requirements that were not addressed by the Final Regulatory Flexibility
Analysis attached to USF/ICC Transformation Order.118 Therefore, we certify that the
requirements of this order will not have a significant economic impact on a substantial number of
small entities. The Commission will send a copy of the order including a copy of this final
certification, in a report to Congress pursuant to the Small Business Regulatory Enforcement
Fairness Act of 1996, see 5 U.S.C. § 801(a)(1)(A). In addition, the order and this certification will
be sent to the Chief Counsel for Advocacy of the Small Business Administration, and will be
published in the Federal Register. See 5 U.S.C. § 605(b).

C.

Congressional Review Act

49.
The Commission will send a copy of this order to Congress and the Government
Accountability Office pursuant to the Congressional Review Act.119

D.

Data Quality Act

50.
The Commission certifies that it has complied with the Office of Management
and Budget Final Information Quality Bulletin for Peer Review, 70 Fed. Reg. 2664 (2005), and
the Data Quality Act, Pub. L. No. 106-554 (2001), codified at 44 U.S.C. § 3516 note, with regard
to its reliance on influential scientific information in the Report and Order in GN Docket No. 09-
191 and WC Docket No. 07-52.120

V.

ORDERING CLAUSES

51.
Accordingly, IT IS ORDERED, that pursuant to the authority contained in
sections 1, 2, 4(i), 201-206, 214, 218-220, 251, 254, and 303(r), and of the Communications Act
of 1934, as amended, and section 706 of the Telecommunications Act of 1996, 47 U.S.C. §§ 151,
152, 154(i), 201-206, 214, 218-220, 251, 254, 303(r), 1302, and pursuant to sections 0.91, 0.131,
0.201(d), 0.291, 0.331, 1.3, and 1.427 of the Commission’s rules, 47 C.F.R. §§ 0.91, 0.131,


115 5 U.S.C. § 601(3) (incorporating by reference the definition of "small business concern" in Small
Business Act, 15 U.S.C. § 632). Pursuant to 5 U.S.C. § 601(3), the statutory definition of a small business
applies "unless an agency, after consultation with the Office of Advocacy of the Small Business
Administration and after opportunity for public comment, establishes one or more definitions of such term
which are appropriate to the activities of the agency and publishes such definition(s) in the Federal
Register."
116 Small Business Act, 15 U.S.C. § 632.
117 See USF/ICC Transformation Order and FNPRM, 26 FCC Rcd at 17742-47, paras. 210-26.
118 See id. at 18324-63, App.O.
119 See 5 U.S.C. 801(a)(1)(A).
120 See Letter from Patrick Halley, FCC, to Marlene Dortch, FCC, WC Docket Nos. 10-90, 07-135, 05-337,
GN Docket No. 09-51, CC Docket Nos. 01-92, 96-45, 03-109, at Apps. B & C (dated Mar. 9, 2012).
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0.201(d), 0.291, 0.331, 1.3, 1.427 and pursuant to the delegations of authority in paragraphs 210,
217, 226 and 1404 of USF/ICC Transformation Order, 26 FCC Rcd 17663 (2011), that this Order
IS ADOPTED, effective thirty (30) days after publication of the text or summary thereof in the
Federal Register.
52. IT IS FURTHER ORDERED, that the Commission SHALL SEND a copy of this
Order to Congress and the Government Accountability Office pursuant to the Congressional
Review Act, see 5 U.S.C. § 801(a)(1)(A).
53. IT IS FURTHER ORDERED, that the Commission’s Consumer and Governmental
Affairs Bureau, Reference Information Center, SHALL SEND a copy of this Order, including the
Final Regulatory Flexibility Certification, to the Chief Counsel for Advocacy of the Small
Business Administration.
FEDERAL COMMUNICATIONS
COMMISSION
Sharon E. Gillett
Chief
Wireline Competition Bureau
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APPENDIX A

Modeling Limits on Reimbursable Operating and Capital Costs

Overview. This appendix describes a methodology for determining carrier-specific limits
on High Cost Loop Support (HCLS) payments to rate-of-return cost carriers with very high
capital expenses (capex) and operating expenses (opex) relative to their similarly situated peers.
Building on the record received in response to the USF/ICC Transformation FNPRM, and the
comments of two peer reviewers,1 the methodology adopted today refines the HCLS calculation
algorithm proposed in the FNPRM.2 This appendix describes both the econometric process used
to establish carrier-specific limits to HCLS payments for rate-of-return cost companies and the
implementation process.
54.
The methodology described herein provides a detailed and implementable
mechanism for examining all rural rate-of-return cost study areas and limiting HCLS payments in
those study areas that have costs higher than the vast majority of their similarly-situated peers.
We use data from all the rural rate-of-return cost carriers.3 We use quantile regression for
parameter estimation rather than ordinary least squares for reasons set forth below. The most
significant change in methodology from that described in the FNPRM is that this analysis creates
two caps, one each on capex and opex, rather than capping eleven different NECA algorithm
steps. Because this methodology builds upon NECA’s existing algorithm for calculating average
loop costs, the revised methodology can be implemented quickly and simply.


1 See Letter from Patrick Halley, FCC, to Marlene Dortch, FCC, WC Docket Nos. 10-90, 07-135, 05-337,
GN Docket No. 09-51, CC Docket Nos. 01-92, 96-45, 03-109, at Apps. B & C (dated March 9, 2012)
(Sanyal Peer Review and Waldon Peer Review, respectively).
2 Connect America Fund; A National Broadband Plan for Our Future; Establishing Just and Reasonable
Rates for Local Exchange Carriers; High-Cost Universal Service Support; Developing a Unified
Intercarrier Compensation Regime; Federal-State Joint Board on Universal Service; Lifeline and Link-Up;
Universal Service Reform—Mobility Fund
; WC Docket Nos. 10-90, 07-135, 05-337, 03-109, CC Docket
Nos. 01-92, 96-45, GN Docket No. 09-51, WT Docket No. 10-208, Report and Order and Further Notice of
Proposed Rulemaking, 26 FCC 17663, 18285-94, App. H (2011) (USF/ICC Transformation Order and
FNPRM
); pets. for review pending sub nom. In re: FCC 11-161, No. 11-9900 (10th Cir. filed Dec. 8,
2011).
3 The analysis is based on 2010 NECA data. See National Exchange Carrier Assoc., Inc., Universal Service
Fund Data, NECA’s Study Results, 2010 Report (NECA 2010 USF Data),
http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-State_Link/Monitor/usf11r10.zip, available at
http://transition.fcc.gov/wcb/iatd/neca.html. Pursuant to section 54.305 of the Commission’s rules, an
acquiring carrier receives support for exchanges acquired from another carrier at the same per-loop support
as calculated at the time of transfer. See 47 C.F.R. § 54.305. Rural carriers who incorporate acquired
exchanges into an existing study area are required to provide separately the cost data for the acquired
exchanges and the pre-acquisition study area. Per operation of Commission rules (47 C.F.R. § 54.305(b)),
the support for the acquired portion of the study area is frozen. See National Exchange Carrier Assoc., Inc.,
NECA’s Overview of Universal Service Fund, Submission of 2010 Study Results, App. F (filed Sept. 30,
2011) (NECA 2010 USF Overview), http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-
State_Link/Monitor/usf11af.zip, available at http://transition.fcc.gov/wcb/iatd/neca.html. The costs
associated with the acquired portion of these study areas are generally lower because the acquired
exchanges were from lower-cost carriers, so it would not be reasonable to add either the lines or the costs
associated with those lines into the methodology as they would tend to make other cost company costs look
high by comparison.
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55.
Background. Today, cost companies eligible for HCLS file with NECA annual
detailed cost data, pursuant to Part 36, at the study area level reporting their costs in many
different cost categories.4 The cost categories are then fed into NECA’s 26-step Cost Company
Loop Cost Algorithm.5 The early algorithm steps calculate intermediate values (based on the
reported cost categories) and feed into the later algorithm steps. Algorithm step 25, which
calculates the carrier’s total unseparated cost for that study area, sums several of the preceding
algorithm steps and then feeds into algorithm step 26, which computes the carrier’s total
unseparated cost per-loop for that study area by dividing the value for algorithm step 25 by the
number loops in the study area.6 HCLS for each study area is then calculated by the Expense
Adjustment Algorithm.7 This algorithm ultimately determines HCLS payments based on a study
area’s cost per-loop compared to the nationwide average cost per-loop.8
56.
Methodology for Imposing Limits. Appendix H of the FNPRM proposed to
create 11 caps (four capex caps and seven opex caps).9 Several commenters argued that we
should reduce the number of caps because efficient carriers might limit their total expenditures by
spending a large amount in one cost category to avoid spending even more money in other
categories.10 Additionally, some commenters and one of the peer reviewers suggested the use of
a single cap, that is, a single dependent variable in the cost regressions, noting that the 90th
percentile of total cost is not the sum of the 90th percentiles of cost components.11
57.
For the reasons described in the HCLS Benchmarks Implementation Order, we
conclude that using two caps, one for capex and one for opex, provides the appropriate balance
between identifying unusually high costs and providing carriers operational flexibility.12
58.
To implement this revised framework, the updated methodology separates
algorithm step 25 (Total Unseparated Costs) into total capex and total opex cost components.
The current algorithm step 25 sums algorithm steps 13 through 24. As a result of the updated
methodology, capex components are now summed into algorithm step 25A and opex components
are summed into algorithm step 25B. Consistent with the methodology proposed in Appendix H,


4 See NECA 2010 USF Overview, App. A, at http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-
State_Link/Monitor/usf10af.zip.
5 See NECA 2010 USF Overview, App. B, at http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-
State_Link/Monitor/usf10af.zip.
6 Although NECA labels each algorithm step with a line number, we continue to use the word “step” in our
description of the methodology to avoid possible confusion of lines with loops.
7 See NECA 2010 USF Overview, App. B, at http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-
State_Link/Monitor/usf10af.zip.
8 The national average cost per loop used in the HCLS support calculation is set to ensure that total HCLS
disbursements stay within the HCLS cap that year rather than the actual average loop cost. See 47 C.F.R.
§§ 36.603(a), 36.622. Rural carriers receive support equal to 65 percent of their costs in excess of 115
percent of the NACPL. Additionally, carriers receive support equal to 75 percent for costs in excess of 150
percent of the NACPL. See 47 C.F.R. §§ 36.601-.631.
9 USF/ICC Transformation Order and FNPRM, 26 FCC Rcd at 18288-89, App. H, paras. 15-16.
10 Accipiter Comments at 19 and NASUCA Comments at 52. Rural Association Comments, App. E, at 6-7
(Koenker).
11 Nebraska Rural Comments/ Rural Associations Comments at App. E, 5 (Koenker); Sanyal Peer Review
at 1.
12 See supra HCLS Benchmarks Implementation Order at para. 15.
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a company whose actual costs for algorithm step 25A or algorithm step 25B are above the 90th
percentile for that cost, compared to similarly situated companies, would be limited to recovering
amounts that correspond to the 90th percentile of capex or opex costs, i.e. the costs that ninety
percent of similarly situated companies would be estimated to have by the regression equation.13
Algorithm step 25C becomes the new Total Unseparated Costs by summing algorithm steps 25A
and 25B. It then feeds into algorithm step 26 (Study Area Cost per Loop) and the subsequent
Expense Adjustment Algorithm as before. We identify the capex and opex components below.
59.
Use of Quantile Regression. As proposed in the FNPRM, we use quantile
regression to estimate the caps for the capex and opex cost components.14 The goal of the
regression methodology is to identify study areas that have capex and opex costs that are much
higher than their similarly-situated peers and to cap their cost recovery at amounts that are no
higher than the vast majority of similarly-situated study areas. Quantile regression allows us to
directly estimate the 90th percentile costs for study areas with given characteristics. The critical
values become the capex and opex caps.
60.
We conclude that quantile regression is preferable to ordinary least squares for
this application. Ordinary least squares regression cannot be used to identify the proper critical
values in the tail of the cost distribution without making strong assumptions about the nature of
the cost distribution, in particular, that error terms are Gaussian (normally distributed) and
homoscedastic.15 In contrast, quantile regression requires no assumptions about the error terms.
This is important because the error terms of the ordinary least squares regressions for capex and
opex are both heteroscedastic and non-normal.16 While methods exist to estimate corrections for
heteroscedasticity and non-normal error terms in ordinary least squares regression, these would
require additional computational steps without improving the precision of the quantile estimate.
61.
Quantile regression is also more resistant to the presence of outliers than
ordinary least squares, which can produce biased parameter estimates when outliers are present.17
Thus, quantile regression parameter estimates are more stable than ordinary least squares
parameter estimates if the data include outliers.18 And although ordinary least squares has
methods available for dealing with outliers, such as excluding them from the analysis or using


13 The term “similarly-situated peers” means that, based on data from all the carriers in the analysis, if there
were (hypothetically) 100 study areas with independent variable values that were the same as those with the
study area in question, 90 of them would be expected to have capex and opex costs equal to or less than the
90th percentile prediction.
14 Both peer reviewers agreed that quantile regression is the proper tool for this analysis. Waldon Peer
Review at 1 and Sanyal Peer Review at 1. See also, Rural Associations Comments at App. E, 7 (Koenker).
15 Even though OLS provides unbiased parameter estimates in the presence of heteroscedasticity, the
standard errors are not unbiased. Because the standard errors would be required to determine which
observations lie above the critical cutoff values, in the presence of problems such as heteroscedasticity,
some adjustment to the standard errors would be needed.
16 For the capex model, we ran the regressions using ordinary least squares and performed two tests for
heteroscedasticity: the White test and the Breusch-Pagan / Cook-Weisberg test. Both tests clearly rejected
the null hypothesis of homoscedasticity with a p-value of less than .0001. Further, the Cameron &
Trivedi's decomposition of IM-test shows that the error terms are not normal – the error terms suffer from
kurtosis (p-value=0.0051), and skewness (p-value = 0.0017), which are statistically significant.
17 G.S. Madalla, Introduction to Econometrics, 2nd Ed. 88 (1992) (Macmillan Publishing Co).
18 Lingxin Hao and Daniel Q. Naiman, Quantile Regression 20 (2007) (Sage Publications).
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dummy variables, these methods generally require an exercise of judgment to identify outliers.
Quantile regression largely avoids the need to make such determinations.
62.
Another significant advantage of quantile regression is that it allows the
independent variables to have different effects on the dependent variable in the different
quantiles.19 Thus, for example, as the percentage of a study area that is national parkland
increases (holding everything else constant), the size of the study area’s cost increase could differ
based on where it falls in the cost distribution of similarly-situated study areas (which quantile it
is in). This is not allowed in ordinary least squares, which assumes that the marginal effect is the
same on all study areas. Given that we are examining study areas with high costs relative to other
study areas conditioned on the independent variables used in the design, this is a helpful property.
63.
Use of the Log-Log Specification. As proposed in the FNPRM, we use the log-
log specification, and therefore take the natural log of the variables most sensitive to scale effects.
For the dependent variables, the capex regression uses the natural log of capex, and the opex
regression uses the natural log of opex. We also use the natural logs of all independent variables
used in the methodology except those that are dummy variables, a pure index, or a percentage
(namely, Climate, Difficulty, PctTribalLand, PctPark, Alaska, MW, and NE).
64.
Some commenters and a peer reviewer argued that the Commission failed to
demonstrate the need for taking the natural logs for both the dependent and independent
variables.20 Additionally, a commenter argued that doing so was appropriate when the dependent
variable is known to have a multiplicative relationship, and therefore the regressions should use
the variables in levels (i.e., that we should not take the natural log of the variables) or that we
should examine cost per loop.21 Another commenter, as well as both peer reviewers, noted that
the manner in which zeros are dealt with, even when using quantile regression, can affect the
results.22
65.
Because our econometric specification is a reduced form, taking the logs of both
the dependent and independent variables is acceptable so long as the resulting relationship is
linear. We disagree with commenters who suggested that we should leave the variables in levels.
Figure 1 shows that the scatter plot of (the level of) opex versus (the level of) the number of loops
is not obviously linear. In contrast, Figure 2 displays the scatter plot of the natural log of opex
versus the natural log of loops, and shows that the relationship is linear. Further, in a simple
ordinary least squares regression of opex on the number of loops and the natural log of the
number of loops, both variables are significant. This indicates that the relationship between opex
and loops is nonlinear.
66.
Further, some commenters argued that we should predict costs per loop and that
if we took this approach, density would become an important independent variable.23 Figure 3


19 See Fig. 4 and surrounding text in “Quantile Regression” by Koenker and Hallock, Journal of Economic
Perspectives, Volume 15, Number 4, Fall 2001, Pages 143–156.
20 Nebraska Rural Comments Pages 41-45; NASUCA Comments at 54; Sanyal Peer Review at 3; Waldon
Peer Review at 2.
21 Nebraska Rural Independent Companies (NRIC) Comments at 42.
22 Rural Associations Comments at App. E, 8 (Koenker); Sanyal Peer Review at 3; Waldon Peer Review at
2.
23 NRIC Comments at 14-15.
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shows that opex per loop as a function of density is nonlinear.24 In contrast, Figure 4 shows that
the relationship between the natural log of opex and density is linear. Similarly, the graph of
capex versus road miles does not appear to be linear, but natural log of capex versus the natural
log of road miles does. We thus conclude that the log transformation of the dependent and
independent variables that are scale sensitive is the appropriate specification.
67.
Finally, the reduction in the number of regressions in the final methodology
eliminates the problem of taking the natural log of zero in the dependent variable. Because the
final methodology uses two regressions rather than eleven, the values of the dependent variables
are never less than or equal to zero, as was the case for many of the values in the algorithm step 8
regression as originally proposed in the FNPRM. Further, none of the independent variables that
we use have zero values.25
68.
Fit of the Regression Model. Some commenters argued that the regressions in
the proposed methodology suffered from low pseudo R-square values, and therefore the proposed
methodology should be abandoned.26 Another commenter asserted that alternative models (i.e.,
those that were based on levels or on cost per loop) were superior to the proposed model because
the R-square values were higher when using levels or cost per loop.27
69.
We conclude that our revised methodology offers sufficient predictive power.
Although the pseudo R-square values in the proposed methodology ranged from 0.2745 to
0.5863, the pseudo R-square values in the revised methodology are .6684 for capex and 0.6234
for opex. We conclude that our final specification has sufficient predictive power to provide a
reliable method for setting reasonable limits on carriers’ costs. We also note that because the
dependent variables are different, and because we are performing quantile regression rather than
ordinary least squares regression – the method proposed by NRIC – we cannot directly compare
the pseudo R-square values from our methodology to the R-square values from commenters’
alternative specifications.28
70.
Elimination of Independent Variables from Specification. If a variable is
significant in either the capex or opex regression, we include it in both regressions. We are
cognizant of Dr. Koenker’s comments that in quantile regression (as in ordinary least squares
regression), the inclusion of non-significant variables can inflate the variance of the prediction


24 This is unsurprising: Chart 2 (page 14) in NRIC’s Capex Study shows a non-linear relationship as well.
See Letter from Thomas Moorman, Counsel to Nebraska Rural Independent Companies, to Marlene H.
Dortch, Secretary, FCC, WC Docket Nos. 10-90, 05-337, GN Docket No. 09-51, Attach., at 14 (Nebraska
Rural Independent Companies’ Capital Expenditure Study: Predicting the Cost of Fiber to the Premise)
(dated Jan. 7, 2011) (NRIC’s Capex Study).
25 In testing land area, housing units and census blocks with breakouts for urbanized areas or urbanized
clusters, we used the totals of these variables and the percent that were rural. All study areas have positive
values for land area, census blocks and housing units, so we were able to calculate the natural logs for all
observations for these variables. Ultimately, however, census blocks and housing units were not included
in the final methodology. Also, a peer reviewer noted that when calculating the caps, the methodology as
proposed in the FNPRM failed to account for the fact that we added one to all the dependent variables for
which we took the natural logs. See Waldon Peer Review at 3. Because we did not need to add 1 to any of
the dependent variables in the refined methodology that we now adopt, that situation is impossible here.
26 NASUCA Comments at 41, 49.
27 NRIC at 15.
28 W. Greene, Econometric Analysis, 2nd Ed. 54 (1993) (Macmillan Publishing Co).
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(yet leave the prediction unbiased).29 Nevertheless, we keep variables that are significant in
either regression in both regressions because they can have offsetting effects in the regressions.
For example, a carrier facing close-to-the-surface bedrock (which would make trenching more
difficult than usual) may find it efficient to use an aerial solution rather than to trench through
bedrock. The presence of close-to-the-surface bedrock could then lower the carrier’s capex cost
but raise its opex cost because cables on poles may be more costly to maintain. Thus, bedrock
could raise that carrier’s opex costs but could plausibly lower that carrier’s capex expenditures. If
we omitted bedrock from the capex regression, we could be biasing the coefficient values in the
regression and therefore biasing the predicted 90th percentile values for capex.
71.
Further, we note that unlike the regressions in the proposed methodology, the
vast majority of the variables in the updated methodology’s regressions are significant in both
regressions. We also note that adding statistically insignificant variables to our regressions do not
bias our predictions.30 In light of all these considerations, we therefore believe it is better to
include variables that are significant in either of the regressions in both.
72.
In its Updated Opex Study, NRIC suggests creating a cap that uses not just the
regression coefficients, but also adds a standard deviation to each regression coefficient.31 We
decline to do so here. Adding the estimated standard error to the parameter estimates is a non-
standard way of creating a confidence interval in the context of quantile regression. In contrast,
using the regression quantiles methodology gives a direct unbiased estimate of the 90th percentile
predictions for capex and opex.32
73.
Use of Census Block Centroids. Consistent with the methodology set forth in
the FNPRM, we determine which census blocks are in each study area by using the census
blocks’ centroids. This enables us to generate certain demographic variables for each study area,
such as the number of housing units in a study area. Because study area boundaries do not
always coincide with census block boundaries, some census blocks will fall into two different
study areas. Where a census block’s centroid falls inside the study area boundary, we associate
that block with that study area, and if a census block’s centroid falls outside of the study area
boundary, we do not.
74.
Some commenters suggested that associating census blocks with study areas
based on the census block’s centroid can distort population and/or housing unit counts.33 While
NRIC argues that such errors do not necessarily cancel each other out, they did not have a
material impact on the cost caps in the case of Nebraska.34 We conclude that our approach is
reasonable. We could split census blocks that cross study area boundaries into pieces and then


29 Rural Associations Comments at App. E, 7 (Koenker).
30 On this point Koenker agreed. See id.
31 See Letter from Cheryl L. Parrino, Parrino Strategic Consulting Group, to Marlene H. Dortch, Secretary,
FCC, GN Docket No. 09-51, WC Docket Nos. 10-90, 05-337, CC Docket No. 01-92, Attach. 2 at 3-4
(Operating Expense Study Sponsored by the Nebraska Rural Companies: Update to Predicting the
Operating Expenses of Rate-of-Return Telecommunications Companies) (dated Sept. 29, 2011) (Updated
Opex Study).
32 Another option would be to adjust the capex and opex 90th percentile predictions by a standard error. We
decline to do this for the same reason we decline to add a standard error to each variable coefficient.
33 Accipiter Comments at 14, Moss Adams Comments at 14, NRIC Comments at 30, Nemont Reply
Comments at 3.
34 NRIC Comments at 30-33.
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assume that end-user locations are spread evenly within census blocks so that we proportionately
attribute housing units to study areas. This would increase computational complexity but not
necessarily accuracy because end-user locations are not uniformly distributed within census
blocks. We further note that the vast majority of study areas have many blocks and therefore
such errors would tend to cancel each other out. Of the 726 study areas covered by the updated
methodology have 1.1 million census blocks in them, so on average, each study area has about
1,567 census blocks. The smallest number of census blocks in a study area is 26, the 5th
percentile is 132, and the 10th percentile is 187. Therefore, the vast majority of study areas would
not be affected by this issue. Also, there is only one variable that uses the number of housing
units (which is derived from the census blocks in the analysis), the natural log of density (see
LnDensity below), so the effect of any error should be small.
75.
Dependent Variables. As described above, the dependent variables in the
regressions are the natural log of the capex components and the natural log of opex components
of algorithm step 25. Below we define capex and opex, but in short, we assign all the constituent
parts of algorithm step 25, which calculates the carrier’s total unseparated cost for that study area,
to either capex or opex. Because we are now aggregating capex costs into a single capex
variable, and operational costs into an opex variable, variations in individual capex and opex
components are smoothed. This allows us to include data on all elements of capex and opex
while still achieving good regression fits.
76.
For the purpose of the updated methodology that we adopt today, we define
capex to be the plant-related costs in the current algorithm step 25. We thus include the return to
capital components, which are algorithm step 23 and algorithm step 24.35 We also include
depreciation in capex (algorithm step 17 and algorithm step 18).36 Although accounting
textbooks typically define depreciation as an operating expense, they do so because firms need to
recognize a periodic charge against earnings to expense the declining value of assets over the
estimated life of the assets.37 Because depreciation is inherently tied to the carriers’ asset
investment decisions, we assign it to capex. We note that in its Opex Study, NRIC considered
depreciation to be sufficiently non-operations-based that NRIC took depreciation out of opex.38
Although some commenters urged us to exclude depreciation from the methodology altogether,39


35 Specifically, algorithm step 23 is the return component for cable and wire facility Category 1, and
algorithm step 24 is the return component for central office equipment Category 4.13. Included in these
return components are algorithm steps 7 and 8 (materials and supplies assigned to cable and wire facilities
Category 1 and central office equipment category 4.13 respectively), which are plant-related capital costs,
and which were erroneously considered to be opex in Appendix H.
36 Algorithm step 17 is depreciation and amortization expense assigned to cable and wire facility Category
1. Algorithm step 18 is depreciation and amortization expense assigned to central office equipment
Category 4.13.
37 See, e.g. Williams, Stanga and Holder, Second Edition, Intermediate Accounting, Harcourt Brace
Jovanovich, Inc. [1987] Page 550.
38 See Letter from Paul M. Schudel, Counsel to Nebraska Rural Independent Companies, to Marlene H.
Dortch, Secretary, FCC, WC Docket Nos. 10-90, 07-135, 05-337, 03-109, GN Docket No. 09-51, CC
Docket Nos. 01-92, 96-45, Attach. at 6 (Operating Expense Study Sponsored by the Nebraska Rural
Independent Companies and Telegee Alliance of Certified Public Accounting Firms: Predicting the
Operating Expenses of Rate-of-Return Telecommunications Companies) (dated May 10, 2011) (NRIC’s
Opex Study). The NRIC Capex Study did not use accounting costs, and so it did not directly ascribe
depreciation to capex.
39 See, e.g., NRIC Comments at 59; Moss Adams et al. Comments at 15-18; Chillicothe Comments at 6-9.
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we disagree for two reasons. First, depreciation is a valid measure of plant that goes beyond the
measure of net plant that goes into algorithm steps 23 and 24. Depreciation is a function of not
just the amount of gross plant, but also the useful life of the plant that is used, a meaningful
measure. Second, by including depreciation, we include all the portions of the existing algorithm
step 25.
77.
For the purpose of the updated methodology, we define opex to be the remaining
components of the current algorithm step 25. We include algorithm steps 13 and 14 in opex
because they are maintenance expenses.40 We also include algorithm steps 15 and 16 in opex
because they are network expenses.41 Algorithm step 21 in included in opex because it is
corporate benefits.42 Below we discuss in more detail the other algorithm steps included in opex.
78.
Algorithm step 19 is corporate operations expense, which is limited in
accordance with section 36.621(a)(4) of the Commission’s recently revised rules.43 Although this
step is already limited by the updated formula limiting recovery of corporate operations expenses,
and was excluded in the methodology as proposed in the FNPRM, we now include it in opex
because the goal of the updated methodology is to examine opex in its entirety. Algorithm step
19 uses DL535 and DL550, which are the lesser of the allowable or actual corporate operations
expenses, not the unadjusted corporate operations expenses, so a study area that is affected by
§36.621(a)(4) is not being affected twice by the higher-than-allowable amount.
79.
We similarly include algorithm step 20 (operating taxes) in opex in the revised
methodology. Although the methodology proposed in Appendix H excluded step 20, after further
consideration, we concluded that taxes are an expense that must be paid, just like other
operational expenses.44
80.
Finally, we include algorithm step 22 (rents) in opex.45 This step was excluded
from the proposed methodology in Appendix H because the regression fit was poor. Because we


40 Algorithm step 13 is cable and wire facilities maintenance and algorithm step 14 is central office
equipment maintenance expense assigned to Category 4.13.
41 Algorithm step 15 is network support expense plus general support expenses assigned to cable and wire
facility Category 1 and central office equipment Category 4.13. Algorithm step 16 is network operations
expenses assigned to cable and wire facility Category 1 and central office equipment Category 4.13. These
expenses are not capitalized in accordance with FCC Report 43-04 – Report Definition page 24, available
at http://transition.fcc.gov/wcb/armis/documents/2007PDFs/4304c07.pdf.
42 Specifically, algorithm step 21 is benefits other than corporate operations expense assigned to cable and
wire facility Category 1 and central office equipment Category 4.13.
43 Specifically, algorithm step 19 is corporate operations expense assigned to cable and wire facility
Category 4.13, which is limited in accordance with §36.621(a)(4).
44 We understand that tax rates are beyond a carrier’s control, but so are many other rates and prices, such
as prevailing local wage rates or the prices of electricity and copper. We expect carriers relying on
universal service support, like providers operating in the market, to make efficient investment and
operating decisions in light of these costs, and by estimating the 90th percentile as the basis for the cost
caps, we allow providers substantial flexibility to do so without exceeding the caps. Further, were we to
have a single cap based on total unseparated costs (algorithm step 25) as some commenters suggest rather
than the two existing caps, we would be including taxes. Rural Association Comments, App. E, at 5
(Koenker); Sanyal Peer Review at 1.
45 Specifically, algorithm step 22 is rents assigned to cable and wire facility Category 1 and central office
equipment Category 4.13.
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can now include rents as a part of opex as a whole as opposed to in its own separate category, we
include it in the updated methodology.
81.
Independent Variable Specification. Our reduced-form regression specification
uses as independent variables exogenous factors that we believe affect a study area’s capex and
opex. These variables fall into the following categories: scale, age of plant, customer dispersion,
and geography.46 Additionally, the independent variables we examined and include in this
updated methodology are those that are currently available to the Commission and exist for all
study areas in the regression analysis.
82.
To the extent that we had the requisite data, we also tested other variables that
commenters suggested be included. First we describe the variables we include in the
methodology, then the variables that we examined and ultimately excluded, and finally, the
variables that commenters suggested but that we could not include in the methodology due to data
issues. All geographic independent variables were rolled up to the study area using Tele Atlas
study area boundary data.47 We do not include inputs to the production process (such as
employees) in the regressions because carriers can choose the amount of these inputs. In other
words, carriers with markedly higher costs than their similarly situated peers may be using
substantially more of these inputs.48
83.
Table 1 and Table 2 respectively show descriptive statistics for and correlations
between the variables included in the updated methodology. The regression results are included
in Table 3.49
84.
Scale. We use several variables to measure scale: the number of loops, road
miles, road crossings, and the number of study areas held under common control in the state. All
the scale measures we include in the updated methodology are significant in the opex regression
and all but LnRoadMiles are significant in the capex regression.50


46 Some commenters stated that some variables in Appendix H were not cost drivers or were not good
proxies. Accipiter Comments at 25-26, Moss Adams at 12. This is largely moot because we have mostly
eliminated the variables criticized by the commenters, such as the number of census blocks in rural areas,
from the final methodology. We also point out that is not necessary to have only cost drivers in the
analysis if proxies can be found that are sufficiently correlated with the cost drivers. We used cost-driving
variables directly where available and proxies where necessary.
47 TomTom Telecommunications Suite 2011.09 (formerly Tele Atlas North America). TomTom acquired
Tele Atlas and subsequently re-branded the wire center boundary data. Because commenters refer to the
earlier brand name, for purposes of this appendix and the accompanying order, we refer to the wire center
boundary data as Tele Atlas data. The Tele Atlas wire center boundaries were dissolved to create study
area boundaries. Earlier study area boundary versions and other information were also used to exclude the
acquired portions of study areas that were associated with frozen support. See Letter from Patrick Halley,
FCC, to Marlene Dortch, FCC, WC Docket Nos. 10-90, 07-135, 05-337, GN Docket No. 09-51, CC Docket
Nos. 01-92, 96-45, 03-109.
48 We thus exclude variables that the updated NRIC Opex study included such as employees and net
wireline plant per access line.
49 The data and the code to verify this are available at the following: http://www.fcc.gov/encyclopedia/rate-
return-resources.
50 For the purpose of the updated methodology, we consider a variable to be significant when the p-value is
less than 0.10. While studies often use a cutoff p-value of 0.05, that is generally for statistical inference.
Because we are creating predictions, we wish to be somewhat more inclusive to lessen the chance of
omitted variable bias, so we therefore used the higher p-value.
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85.
Because the number of loops is a direct measure for the scale of the study area,
we include the natural log of the number of loops (LnLoops) in the updated methodology.51 We
expect that the amount of plant a carrier must install will be positively correlated with capex and
opex costs because more loops require more investment and operations cost.52 LnLoops is
statistically significant.
86.
We also include the natural log of the number of road miles (LnRoadMiles),
which is a proxy for total loop length.53 Several commenters argued that some measure of loop
length was an important variable.54 Although some (but not all) cost carriers may report such
data to the Department of Agriculture’s Rural Utilities Service (RUS), such data are both
incomplete and unavailable to the Bureau. We agree with NRIC that cable generally follows
roads, so the number of road miles in a study area should correlate with the cabling required to
serve that area.55
87.
In its Capital Expenditure Study, NRIC predicted that road intersections would
slow fiber construction and impose other costs and found that the number of intersections was a
significant predictor of predicted construction costs.56 We agree that the number of such
crossings is another good proxy for scale and therefore included the natural log of road crossings
(LnRoadCrossings).57
88.
The scale variables (LnRoadMiles) and road crossings (LnRoadCrossings) are
significant in the opex regression, but have the opposite sign from each other. Only road


51 We calculate LnLoops using the 2010 DL060 loop count in NECA’s October 2011 filing. See NECA
2010 USF Data, http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-
State_Link/Monitor/usf11r10.zip, available at http://transition.fcc.gov/wcb/iatd/neca.html. We use DL060
loops because the capex and opex costs we use are associated with all the study area’s loops, not just the
supported DL070 loops.
52 Arguably loops are an input to the production process, which as we emphasize above, should be excluded
from the independent variable list. Because loops are put in at a customer’s request, however, and because
carriers are generally restricted in their ability to refuse such requests pursuant to carrier-of-last-resort
obligations, we do not consider loop counts to be a carrier-controlled cost driver like the number of
employees.
53 For most of the study areas, road miles data come from the ESRI ArcGIS StreetMap
(http://gislab.allegheny.edu/Documents/StreeMap_USA.pdf) (ESRI Street Map). Because ESRI Street
Map does not include data for Guam and American Samoa, we used Tiger files for these study areas, which
because they were generated for Census applications, may be less accurate for transportation applications.
The Tiger files are available at the US 2010 Census website: http://www2.census.gov/cgi-
bin/shapefiles2009/national-files. Because only two study areas were affected, we concluded that using a
separate source data for road miles for these study areas reasonable. We found that the slope on
LnRoadMiles and LnRoadCrossings were unaffected by using the Tiger files for Guam and American
Samoa.
54 See Central Texas at 5 and Accipiter Comments at 26.
55 NRIC Comments at 16.
56 NRIC Capex Study at 10. We believe that maintenance costs would also be higher in the presence of
additional road crossings because of travel delays and the increased costs associated with the dangers of
intersections.
57 NRIC reiterated the usefulness of the road crossing data in its comments. NRIC Comments at 25. Note
that we calculate road crossings rather than intersections because counting intersections is computationally
very burdensome. Two roads that cross at right angles (forming a plus sign) create four crossings. We
believe that road crossings is a good proxy for road intersections.
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crossings are significant in the capex regression.
89.
Our last scale variable is the number of study areas in the state that are owned by
the same holding company or have common control in the state (LnStateSACs).58 We anticipated
that this variable would be a good predictor of capex and opex costs because some expenses
could be shared among study areas. For capex, study areas that are part of a larger organization
(i.e., the study area has more commonly-owned study areas in the state) may allow installation
crews to be deployed more efficiently. For opex, study areas that are part of a larger organization
can share various expenses, especially headquarters-related expenses, which would allow for
some specialization among management employees. We found LnStateSACs to be significant for
both capex and opex.
90.
Age of Plant. Commenters stated that age of plant was an important variable for
two reasons: first, because the cost of recent capital investments is higher due to inflation and
second, because the return component of capital expenses is calculated on net plant, and recent
investment will be depreciated less fully than old plant.59 While the Bureau cannot readily
determine the average age of carriers’ plant, the percentage of the plant that has not yet been
depreciated (PctUndepPlant) should be highly correlated with plant age: more recently installed
plant will be less depreciated.60 Holding all else constant, the less of a carrier’s plant is
depreciated (which yields a higher PctUndepPlant), the higher its capex should be. The intuition
for the effect of PctUndepPlant on opex is ambiguous. We find that this variable is a strong cost
predictor for both capex and opex.
91.
Customer Dispersion. We include three variables that account for customer
dispersion. Many commenters asserted that density was an important cost predictor, and that
their costs are high in part because of the rural areas they serve.61 We therefore expect that
density is negatively correlated with both capex and opex costs. Density (LnDensity) is the
natural log of the following quotient: number of housing units in the study area divided by the
size of the study area in square miles as reported by the Tele Atlas boundaries.62 We find that it is
significant in both regressions.63


58 The holding company/common control ownership information can be found in the Universal Service
Monitoring Report, CC Docket No. 98-202, app. (2011) (HC NECA ILEC Support Data - by Study
Area.xls), available at http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-
State_Link/Monitor/2011_MR_Supplementary_Material.zip. (last visited Feb. 16, 2012)
59 See, e.g., Accipiter Comments at 5, Guadalupe Comments at 3. In its comments, Carriers for Progress in
Rural America (at 6) states that population growth should be added to the model to account for the new
plant associated with new neighborhoods. The variables percentage change in undepreciated plant and
percentage change in loops account for this.
60 We calculate the percentage of the plant that has yet to be depreciated as 100 * DL220 / DL160 (i.e.,
100*net plant/gross plant).
61 See, e.g., Guadalupe Valley Comments at 3, Interbel Comments at 10, Moss Adams Comments at 8.
62 See generally, supra note 47. We also tested LnWtDensity, which accounts for density at the block
level. We calculate this by calculating each census block’s density (housing units in the block divided by
square miles of the block) and then calculating the weighted average density weighting by the number of
housing units in each block. LnWtDensity is the natural log of weighted density. LnWtDensity is not
significant.
63 Because we are using a log-log model, the natural log of density (the number of housing units divided by
square miles) captures the effects of both the size of the study area and the number of housing units. We
(continued....)
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92.
We also include the natural log of the number of exchanges in the study area as a
proxy for customer dispersion (LnExchanges). Although the straightforward measure of density
calculates the average customer density within the study area, the number of exchanges roughly
accounts for the number of population centers within the study area because most population
centers will have their own exchanges. The more population centers (holding other factors
constant), the higher capex and opex costs will be because more cabling will be required to
connect the customers within the study area to each other, and the farther the employees will need
to drive to fix any troubles. The variable LnExchanges is significant in both regressions.
93.
Our final customer dispersion variable accounts for the portion of households in
urban clusters or urbanized areas (PctUrban).64 To the extent that rural carriers also serve
urbanized pockets, we would expect their costs to be higher, holding all other variables (including
road miles) constant, because wage rates may be higher near urbanized areas. We thus expect
PctUrban to be positively correlated to opex, and it is. PctUrban’s effect on capex is less clear:
the labor costs associated with trenching are capitalized, so to the extent that labor near urban
areas is more expensive, the higher capital costs should be. But capitalized labor is only one of
many costs in capex, so the effect may not be strong. PctUrban is positive but not significant in
the capex regression.
94.
Geography. Commenters suggested the inclusion of several geographically-
based variables such as soil type. We agree. When creating many of the indexes for geographic
variables, we took into account the location of roads within the study area because cabling
generally follows roads.65 For these variables we overlaid road data in the study area with our
sources of geographic information and calculated variables that were either percentages, or where
appropriate, averages.
95.
For example, commenters stated that soil type is an important cost predictor.66
We therefore constructed a soil difficulty index (Difficulty). This index is similar to the index in
the NRIC capex study in which soil types were matched with construction difficulty values
established for the Commission’s High Cost Proxy Model (HCPM), which the Commission used
to calculate high-cost support for non-rural carriers.67 The STATSGO2 database we use lists
more soil types than the original STATSGO database, however, so there are many soil types in
the STATSGO2 database for which there are no construction difficulty values from the HCPM.
NRIC tried several options, but settled on assuming the soil difficulty level to be 1 (the lowest
level of difficulty) for those soil types not found in the table.68 Our soil difficulty index builds on
the NRIC methodology. When faced with soil types that do not appear on the original HCPM
list, we interpolate the difficulty rating based on similar soil types in the HCPM list. We
manually associate unmatched soil types from the STATSGO2 data with similar soil texture in


(...continued from previous page)
tested the regressions with the natural log of housing units and the natural log of square miles (but omitting
the natural log of density), and the results were very similar.
64 PctUrban is the ratio of the number of housing units in either urbanized clusters or urbanized areas
divided by the total number of housing units in the study area.
65 See supra para. 86.
66 See, e.g., ATC Comments at 3, Calaveras Comments at 7, Eagle Telephone Comments at 4, Guadalupe
Comments at 2.
67 NRIC Capex study at 9.
68 See Letter from Thomas J. Moorman, Counsel to Nebraska Rural Independent Companies, to Marlene H.
Dortch, FCC, Attach. (dated Jan.27, 2012).
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the original HCPM table, and used the difficulty rating of the similar soil types in the HCPM list
for the new soil type in the STATSGO2 database. The new extended table associates a difficulty
rating for all soil types in the STATSGO2 database.69 We then calculated the average soil
construction value along the roads in each study area.
96.
We find soil difficulty to be a statistically significant predictor in opex.
Although NRIC found that soil difficulty was a significant predictor of construction costs,
Difficulty is positive in our capex, but not significant.70 Although we also expected soil difficulty
to be positive in our capex regression, an alternative hypothesis is that in locations where
trenching is unusually expensive, an efficient carrier may install aerial plant (use poles rather than
trench). This would involve lower capital costs than trenching, but higher future operations costs.
Thus, it is plausible that in the presence of difficult-to-trench soils, carriers experience no obvious
change in capex or, in some circumstances possibly even reduced capex costs.
97.
Because NRIC suggested that we account for close-to-the-surface bedrock, we
calculated the percentage of road miles within each study area where bedrock was within 36
inches of the surface (PctBedrock36).71 The NRIC capex study found that predicted construction
costs were positively associated with close-to-the-surface bedrock, so we might expect that the
coefficient on PctBedrock36 should be positive in the capex regression.72
98.
We find that close-to-the-surface bedrock is significant in the opex regression,
but that it is not significant in the capex regression. This result could occur for the same reasons
as for soil construction difficulty above or because the construction difficulty of bedrock has
already been captured by the soil difficulty variable.
99.
Pointing to the NRIC Capex study, which suggested that construction costs are
higher in areas where the ground is frozen more often, several commenters argued that the
regressions should include a frost index.73 The frost index in the NRIC capex study uses of the
number of frost-free days from the SSURGO data. Unfortunately, this information is not
available for all areas in the STATSGO2 database. We believe that the USDA’s hardiness index
is a useful proxy for this information, and we use it to create a simple index called Climate that is
based on the average annual minimum temperature.74 The lower the minimum temperature, the
more days the ground is likely to be frozen. The higher the index, the fewer frost-free days the
study area would have. Based on the comments in the record, we expected this variable to be
negatively correlated with capex (the higher the index, the more frost-free days the area should
have, so construction costs should be lower).


69 This table is available at http://www.fcc.gov/encyclopedia/rate-return-resources.
70 NRIC Capex Study at 18.
71 The NRIC Capex Study found that predicted construction costs were positively associated with close-to-
the-surface bedrock (Capex Study at 17), and in its comments, NRIC suggested including bedrock
information (NRIC Comments at 24).
72 NRIC Capex Study at 17. NRIC did not include bedrock in its final regression, however.
73 Blooston Comments at 2, Interbel Comments at 10, Nebraska Rural Comments at 21.
74 The hardiness index uses the zone numbers in the 2012 USDA Plant Hardiness Zone Map (available at
http://www.usna.usda.gov/Hardzone/). The index increments by 0.5 for each zone, so Zone 1A is 1.0, zone
1B is 1.5, Zone 2A is 2, Zone 2B is 2.5, etc. This table is available at
http://www.fcc.gov/encyclopedia/rate-return-resources. The Climate index is the average of the index
along the roads in the study area. We also think that the variable climate controls for the length of the
construction season that Moss Adams suggested (Moss Adams Comments at 12).
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100.
The Climate variable (Climate) is positive and has low p-values in the
regressions, which means that it is unlikely to be a spurious result. However, it is positively
correlated with capex and opex.
101.
Commenters also stated that it is more difficult to construct and maintain
networks on tribal lands and in national parks because of permitting and similar issues,75 so we
include two additional variables: (1) the percentage of each study area that is a federally-
recognized Tribal land (PctTribalLand),76 and (2) the percentage of each study area that lies
within a national park (PctParkLand).77
102.
The coefficient for the percentage of the study area that is tribal land
(PctTribalLand) is positive for both capex and opex regressions, but is significant in only the
opex regression. The percentage of the study area that is national park land (PctParkLand) is
positive and significant in both regressions. As can be seen in Table 1, most of the study areas do
not contain either tribal or national park land, and it may be a simple lack of data that causes a
lack of significance for PctTribalLand in the capex regression. Nonetheless, we agree that both
capex and opex costs could be higher in the presence of these factors, so we include them in the
model.
103.
Finally, based on comments in the record that certain areas of the country face
unique circumstances, we tested several regional variables. Alaskan commenters suggested that
Alaska was unique because of its harsh climate and other factors.78 We therefore added the
dummy variable Alaska to the regressions, which equals 1 for the 17 study areas in Alaska and
zero elsewhere.
104.
We also include regional dummies because in its Original Opex study NRIC
found that opex costs were correlated with regions.79 Although NRIC did not include region
dummy variables in the regression, instead opting to use 2005 median home value, which it also
used in its Updated Opex Study, we include region in our updated methodology. We tested the
four census-based regions: Western (West), Midwest (Midwest), Northeast (Northeast) and South
(South). We found that Midwest and Northeast were each significant in at least one regression, so
we include them.
105.
Use of Soil Database Information. Our source for soil data is the U.S. General
Soil Map (STATSGO2) soils database. We selected STATSGO2 as a data source because it
provides data for the entire country. The Soil Survey Geographic Database (SSURGO) soils data
from the Natural Resource Conservation Service (NRCS) that the Nebraska Rural Independent
Companies capex study used to generate soil, frost and wetland variables is an attractive database
because it contains a richer set of soil variables and contains data at a smaller granular area than
the STATSGO2 database. Unfortunately, as can be seen from the graph on page 23 of the NRIC
comments, not only do the SSURGO data not cover Guam or American Samoa, and much of
Alaska, but there are also numerous other holes in the data in many states. Thus, there are many


75 Interbel Comments at 3, New Mexico Exchange Carrier Group at 14-15.
76 Tribal land information is available from the US Census Bureau at http://www.census.gov/cgi-
bin/geo/shapefiles2010/main.
77 National Park data are available at
http://www.bts.gov/publications/national_transportation_atlas_database/2011/.
78 See Alaska Rural Coalition Comments at 17-19; Copper Valley Comments at 5-7.
79 NRIC found that cost was strongly related to region in its Original Opex Study (p 8) but did not include it
in its regression, and in its Updated Opex Study used the 2005 median home values in its regression (p 3).
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study areas in Alaska where there is no SSURGO data and even some conterminous United States
study areas such as the West Kentucky Rural Telephone Coop (Study Area Code 260421) where
there is virtually no SSURGO spatial data. We therefore could not apply the results of a
SSURGO-based model to these companies because the needed data would be missing. We
conclude, therefore, that it is not practical to use the SSURGO data at this time.
106.
Two commenters argue that we should use the SSURGO data for study areas
covered by it and use STATSGO2 for the remaining study areas.80 We have concerns about this
approach for several reasons, and ultimately decline to do so. In particular, the commenters’
proposed approach would mean that those study areas for which the SSURGO data are not
universally available would be treated inconsistently with those for which the SSURGO are
universally available. In addition, it would be challenging to combine the two data sets for those
study areas where we have only some SSURGO data. Given these problems, we conclude that
the implementation and fairness benefits of a nationally uniform approach based on STATS2GO
outweigh the benefits of using SSURGO data for a subset of areas.81 We discuss below the
elements of the STATSGO2 data we use.
107.
Independent Variables Tested But Not Used in the Model. Based on
commenters’ suggestions and the analysis proposed in Appendix H, we tested several additional
variables that were ultimately excluded from the final model because they were not significant for
either capex or opex.82
108.
In its Capex Study, NRIC found that rain frequency increased construction cost
per household.83 Following NRIC’s model, we used the Samson weather station data, and for
each study area, calculated the average number of days per year with greater than 0.5 inches of
rainfall (DaysAbvPt5).84 We found DaysAbvPt5 was not significant in either regression.
109.
We also tested the average slope in study areas (slope) using data in the
STATSGO2 database.85 Our hypothesis was that the steeper the slope, the more difficult it would
be to build and maintain cabling. The coefficient on slope was insignificant (i.e., statistically
indistinguishable from zero) in both regressions and therefore dropped from the model.
110.
We similarly tested the percentage of the study area that was water (PctWater),
but we did not include it in the updated model because it was insignificant in both regressions.
This is unsurprising. The proposed model included PctWater to account for the fact that cabling
may have to be run around bodies of water, but the updated model accounts for the number of
road miles (as a proxy for loop length), so the additional cabling associated with routing around
water has already been accounted for.


80 NRIC Comments at 24 and NASUCA Comments at 46.
81 We note that the Commission’s hybrid cost proxy model, which was used to estimate forward-looking
costs for the non-rural high-cost support mechanism, uses an earlier version of the STATSGO2.
82 We include these variables in the data that we posted on the web so that others can verify our results.
83 NRIC Capex Study at 17.
84 For those study areas with one station, the value (for the number of days per year with greater than 0.5
inches of rainfall) for that station was used. For those study areas with more than one station, the average
of the values was used. For those study areas without a station, the nearest station was used. For those
study areas that were non-conterminous, each polygon (i.e., piece) of the study area was treated as its own
study area (for calculating the rainfall statistic), and then the weighted mean value across all the study
area’s polygons was calculated using the polygon’s square miles as the weight.
85 We calculated the average of the absolute value of slope along the road segments in the study area.
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111.
We tested the percentage of road miles where the water table was within 36
inches of the surface (PctWaterTable36).86 We found the variable PctWaterTable36 to be weakly
significant in opex, but it had an implausible negative sign in both the capex and opex
regressions. Because of the sign issue and because inclusion of the variable does not markedly
improve the fit, we exclude it from the model so as not to lower the cap for study areas with high
water tables.
112.
Accipiter suggested adding the percentage change in loops (PctLoopChange) to
account for study areas that are growing, because growing carriers “are prone to have unique cost
structures.”87 We believe thet PctUndepPlant proxies for this, but out of an abundance of
caution, we tested PctLoopChange, but found that it was insignificant, suggesting that
PctUndepPlant is proxying for the unique cost structures that Accipiter is concerned about.88
113.
Based on NRIC’s updated opex regression, we tested statewide median house
values,89 but found them to be insignificant.90 This is unsurprising because statewide values
include mostly urban houses. Our regional independent variables, however, helped capture the
intended effect.
114.
We also tested the natural log of the number of stream crossings
(LnStreamCross), which could increase construction costs in the same way that road crossings do.
We found LnStreamCross to be significant and negative in opex, but insignificant in capex.
Because the coefficient was an implausible sign in the opex regression without an offsetting
plausible coefficient in the other regressions, we omitted LnStreamCross from both regressions.91
115.
The proposed model also included the number of census blocks in the study
area.92 Although the natural log of the total number of census blocks (LnBlocks) was weakly
significant in the opex regression, it was not significant in the capex regression. Although we
generally included variables that were significant in at least one regression in both regressions,
we omitted census blocks from the updated model regressions for two reasons. First, commenters
did not think that the number of blocks was a good proxy for density.93 Also, we are now
accounting for customer dispersion and density directly through independent variables
LnRoadMiles, LnRoadCrossings and LnDensity.


86 The locations of close-to-the-surface water table within 36 inches of the surface come from the
STATSGO2 database.
87 Accipiter Comments at 23-24.
88 We calculated PctLoopChange as the percentage change of DL060 loop count between 2009 and 2010.
For the observations that converted from being average schedule to cost companies (and therefore we did
not have DL060 loop counts for the prior year), we instead used the percentage change in DL070 loops,
which we believe is an excellent proxy for the percentage change in DL060 loops.
89 NRIC’s intent in including house values was to proxy for local “cost of living differences.” NRIC
Updated Opex Study at 3.
90 See http://www.census.gov/hhes/www/housing/census/historic/values.html
91 U.S. Department of the Interior, U.S. Geological Survey, National Hydrography Dataset, last visited Feb.
1, 2012, available at http://nhd.usgs.gov/index.html. As we did with road crossings data, we intersected
stream data with roads to find the number of stream crossings in the study area.
92 USF/ICC Transformation Order and FNPRM, 26 FCC Rcd at 182, App. H, para 24. In the proposed
methodology, the number of blocks was broken out by whether they were in urbanized areas, urbanized
clusters or nonurban (rural) areas.
93 Accipiter Comments at 25-26, Moss Adams at 12.
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116.
Unavailable Independent Variables. Several carriers suggested additional
variables to the regression analysis, but we were unable to include them because the data were
either unavailable to the Commission, nonpublic, or we could not generate data at the study area
level. We recognize that some of the unavailable variables could be significant if they could be
included, but given the other enhancements made to the regressions described herein, we
conclude that the methodology is adequate to identify cost outliers among similarly situated
companies.
117.
The NRIC capex study postulated that the presence of wetlands would increase
construction costs because of need for additional “approvals and specialized techniques.”94 It
found that wetlands were positively correlated with increased predicted construction costs. As
NRIC points out, however, wetlands data are not available for Colorado, Wisconsin and Montana.
Since our objective is to develop a methodology that applies equally to all cost carriers, we could
not include wetlands data in the updated methodology.95
118.
Similarly, commenters suggested the following additional variables that, if not
already proxied in the model, could not be used because they were unavailable to the
Commission, nonpublic, or data could not be generated at the study area level: age of
investment;96 broadband speed capability;97 cable route miles or cable sheath miles;98 status as
carrier of last resort;99 copper versus fiber networks;100 cost of living and labor costs;101
environmental; legal and regulatory costs;102 loop length/average loop length;103 right of way
costs and vacant lots;104 and weather patterns.105


94 NRIC Capex study at 10.
95 In its Capex Study, NRIC uses SSURGO data to create proxies for wetlands data where it does not exist,
but because SSURGO data does not cover the entire country and we are therefore not able to use it, we
could not create that proxy.
96 Interbel Comments at 10. Study areas submit a variety of information on plant, but we cannot calculate
the age of investment from it. Investment age, however, is proxied by PctUndepPlant.
97 Guadalupe Comments at 3. While the Bureau has access to carriers’ FCC Form 477 filings, which
contain broadband speed information for each filer, many carriers file their Form 477 at the holding
company level within a state rather than at the study area code level, so matching up the Form 477 filings
with the study area code would be challenging in some cases. Additionally, the data are nonpublic, and
therefore they could not be published for others trying to replicate the regression results.
98 Guadalupe Comments at 3. Some, but not all, rate-of-return cost carriers report this information to RUS,
but there is no universally-available source of cable sheath or route miles. Cable mileage is proxied by
LnRoadMiles.
99 Guadalupe Comments at 5 and Washington Independent Tel comments at 5. We do not have a source for
which states require study areas to be carriers of last resort. Further, the obligations imposed on a carrier of
last resort can vary by state.
100 Carriers for Progress Comments at 7. We are unaware of a source for this information.
101 Guadalupe and Moss Adams suggested labor costs. Guadalupe Comments at 3; Moss Adams Comments
at 8. We do not have cost of living or labor rate data with sufficient geographic granularity to create a
meaningful index. We note that cost of living and labor rates in rural areas may be less than in urban areas,
so we expect that statewide data would not be helpful. We tested this assumption by including statewide
median house values in the regression, but the variable was not significant. Our regressions instead use
regional variables to proxy for such variations in labor costs.
102 Carriers for Progress Comments at 8. We are not aware of a direct source for such information; instead,
we use the regional, PctParkLand and PctTribalLand variables to proxy for such costs. We considered
(continued....)
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119.
One commenter argues that the Bureau’s methodology should include variables
that are not universally available and that it is better to comprehensively study a representative
sample of study areas and apply the results to the wider population of study areas.106 The
commenter does not specify, however, how the Bureau could apply that knowledge to study
areas for which the information is unavailable.
Implementation. For each study area, the regressions will be used to generate the 90th
percentile predicted values for both the natural log of capex and the natural log of opex. These
values will then be converted back to “levels” by using the inverse of the natural log function.
The lower of the study area’s original algorithm step 25A and the level of the predicted
90th percentile capex value will be retained in algorithm step 25A. Similarly, the lower of the
study area’s original algorithm step 25B and level of the predicted 90th percentile opex value will
be retained in algorithm step 25B. These values will then be summed in algorithm step 25C,
which will feed into algorithm step 26.


(...continued from previous page)
using dummy variables for individual states, but that would significantly benefit the study areas in those
states that had few study areas in the regression, because any inefficiency of that carrier would be picked up
by the dummy variable.
103 Central Tex Comments at 7, Midvale Tel Comments at 5, and Washington Independent Tel Comments
at 3. Some, but not all, rate-of-return cost carriers report this information to RUS, but there is no
universally available source of average loop length. Our regressions use LnRoadMiles to proxy for loop
length.
104 Guadalupe Valley Comments at 3. We are unaware of sources of data for these variables. Our
PctParkLand and PctTribalLand variables proxy for right-of-way costs.
105 Moss Adams Comments at 12. Because weather covers so many things, such as wind, temperature,
rainfall and other attributes, we could not address such a vague suggestion. Above, we discuss the weather
features that we include in the updated methodology: temperature and rainfall.
106 NRIC Comments at 19-20.
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Table 1

Descriptive Statistics For The Raw Data

Std
10th
90th
Variable
N
Mean
Dev.
Min
Pctile
Median
Pctile
Max
LnCapex
726
13.78
1.27
9.01
12.15
13.83
15.41
16.93
LnOpex
726
14.11
1.03
10.29
12.75
14.16
15.38
17.03
LnLoops
726
7.81
1.20
3.00
6.33
7.88
9.28
11.18
LnRoadMiles
726
6.55
1.34
1.88
4.86
6.45
8.43
10.53
LnRoadCrossings
726
8.00
1.23
4.64
6.42
7.94
9.64
11.46
LnStateSACs
726
0.36
0.63
0.00
0.00
0.00
1.39
3.04
PctUndepPlant
726
33.85
14.81
-6.26
16.87
31.96
53.36
88.63
LnDensity
726
2.01
1.59
-4.27
-0.10
2.23
3.73
7.02
LnExchanges
726
1.18
0.98
0.00
0.00
1.10
2.48
4.33
PctBedrock36
726
0.06
0.14
0.00
0.00
0.00
0.22
0.89
Difficulty
726
1.06
0.19
1.00
1.00
1.00
1.16
2.81
Climate
726
6.20
1.59
1.67
4.37
6.00
8.46
12.65
PctTribalLand
726
9.03
24.81
0.00
0.00
0.00
28.11
100.00
PctParkLand
726
0.64
3.86
0.00
0.00
0.00
0.02
47.81
PctUrban
726
9.17
19.46
0.00
0.00
0.00
37.40
95.38
Alaska
726
0.02
0.15
0.00
0.00
0.00
0.00
1.00
Midwest
726
0.41
0.49
0.00
0.00
0.00
1.00
1.00
Northeast
726
0.08
0.27
0.00
0.00
0.00
0.00
1.00
41

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DA 12-646

Table 2

Correlation Coefficients

Ln
Ln
Ln
LnRoad
LnRoad
LnState
PctUnDep
Ln
Ln
Pct
Pct
Pct
Pct
Variable
Opex
Capex
Loops
Miles
Crossings
SACs
Plant
Density
Exhanges
Bedrock36
Difficulty
Climate
TribalLand
ParkLand
Urban
Alaska
Midwest
Northeast
LnOpex
1.00
0.88
0.87
0.58
0.70
-0.12
0.04
0.13
0.60
0.05
0.11
0.25
0.08
0.04
0.28
0.03
-0.19
-0.10
LnCapex
0.88
1.00
0.80
0.59
0.69
-0.14
0.32
0.09
0.60
0.06
0.11
0.15
0.05
0.02
0.23
-0.03
-0.08
-0.17
LnLoops
0.87
0.80
1.00
0.52
0.67
0.03
-0.13
0.34
0.58
-0.05
0.08
0.14
0.00
-0.02
0.35
-0.03
-0.13
0.04
LnRoadMiles
0.58
0.59
0.52
1.00
0.94
-0.13
-0.01
-0.51
0.79
0.07
0.13
-0.05
0.04
0.06
-0.06
-0.02
-0.13
-0.22
LnRoadCrossings
0.70
0.69
0.67
0.94
1.00
-0.12
-0.04
-0.26
0.76
0.06
0.14
0.09
0.03
-0.02
0.10
-0.10
-0.15
-0.21
LnStateSACs
-0.12
-0.14
0.03
-0.13
-0.12
1.00
-0.26
0.16
-0.08
-0.05
0.05
-0.17
-0.07
-0.01
0.05
-0.04
0.12
0.13
PctUnDepPlant
0.04
0.32
-0.13
-0.01
-0.04
-0.26
1.00
-0.10
-0.04
0.08
-0.01
-0.07
0.01
0.01
-0.02
0.00
0.15
-0.19
LnDensity
0.13
0.09
0.34
-0.51
-0.26
0.16
-0.10
1.00
-0.32
-0.15
-0.05
0.26
-0.12
-0.26
0.39
-0.38
0.06
0.25
LnExchanges
0.60
0.60
0.58
0.79
0.76
-0.08
-0.04
-0.32
1.00
-0.01
0.11
-0.11
0.11
0.08
-0.09
0.09
0.00
-0.09
PctBedrock36
0.05
0.06
-0.05
0.07
0.06
-0.05
0.08
-0.15
-0.01
1.00
0.13
0.17
0.17
0.12
-0.01
0.07
-0.22
-0.02
Difficulty
0.11
0.11
0.08
0.13
0.14
0.05
-0.01
-0.05
0.11
0.13
1.00
0.11
0.19
-0.01
-0.01
-0.05
-0.10
-0.08
Climate
0.25
0.15
0.14
-0.05
0.09
-0.17
-0.07
0.26
-0.11
0.17
0.11
1.00
0.06
-0.09
0.22
-0.22
-0.53
-0.18
PctTribalLand
0.08
0.05
0.00
0.04
0.03
-0.07
0.01
-0.12
0.11
0.17
0.19
0.06
1.00
0.04
-0.02
0.16
-0.18
-0.10
PctParkLand
0.04
0.02
-0.02
0.06
-0.02
-0.01
0.01
-0.26
0.08
0.12
-0.01
-0.09
0.04
1.00
0.00
0.44
-0.12
0.00
PctUrban
0.28
0.23
0.35
-0.06
0.10
0.05
-0.02
0.39
-0.09
-0.01
-0.01
0.22
-0.02
0.00
1.00
0.01
-0.05
-0.01
Alaska
0.03
-0.03
-0.03
-0.02
-0.10
-0.04
0.00
-0.38
0.09
0.07
-0.05
-0.22
0.16
0.44
0.01
1.00
-0.13
-0.05
Midwest
-0.19
-0.08
-0.13
-0.13
-0.15
0.12
0.15
0.06
0.00
-0.22
-0.10
-0.53
-0.18
-0.12
-0.05
-0.13
1.00
-0.24
Northeast
-0.10
-0.17
0.04
-0.22
-0.21
0.13
-0.19
0.25
-0.09
-0.02
-0.08
-0.18
-0.10
0.00
-0.01
-0.05
-0.24
1.00
42

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Table 3

Capex (LnCapex) Regression

Variable
Coef.
Std. Err.
t
P>|t|
LnLoops
0.788
0.071
11.15
0.00
*
LnRoadMiles
-0.208
0.136
-1.53
0.13
LnRoadCrossings
0.240
0.091
2.64
0.01
*
LnStateSACs
-0.070
0.043
-1.65
0.10
*
PctUndepPlant
0.031
0.002
18.39
0.00
*
LnDensity
-0.158
0.072
-2.20
0.03
*
LnExchanges
0.118
0.061
1.94
0.05
*
PctBedrock36
-0.072
0.156
-0.46
0.64
Difficulty
0.118
0.087
1.36
0.17
Climate
0.089
0.030
2.99
0.00
*
PctTribalLand
0.0005
0.001
0.47
0.64
PctParkLand
0.018
0.005
3.71
0.00
*
PctUrban
0.001
0.002
0.34
0.73
Alaska
-0.6223
0.337
-1.85
0.07
*
Midwest
0.092
0.091
1.01
0.31
Northeast
-0.309
0.124
-2.49
0.01
*
Constant
6.039
0.416
14.51
0.00
*
N = 726 Pseudo R2 = .6684
Notes:
An * indicates significance at the 0.10 level.
P-values are based on Wald statistics.
Values are rounded. More precise coefficient values are posted at http://www.fcc.gov/encyclopedia/rate-
return-resources.
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Table 3 (contd.)

Opex (LnOpex) Regression

Variable
Coef.
Std. Err.
t
P>|t|
LnLoops
0.596
0.037
15.97
0.00
*
LnRoadMiles
-0.247
0.086
-2.87
0.00
*
LnRoadCrossings
0.272
0.081
3.37
0.00
*
LnStateSACs
-0.078
0.035
-2.22
0.03
*
PctUndepPlant
0.008
0.001
6.47
0.00
*
LnDensity
-0.128
0.034
-3.72
0.00
*
LnExchanges
0.125
0.032
3.94
0.00
*
PctBedrock36
0.279
0.098
2.84
0.01
*
Difficulty
0.114
0.057
2.02
0.04
*
Climate
0.135
0.020
6.91
0.00
*
PctTribalLand
0.002
0.001
2.79
0.01
*
PctParkLand
0.006
0.004
1.65
0.10
*
PctUrban
0.002
0.001
2.52
0.01
*
Alaska
0.299
0.155
1.92
0.06
*
Midwest
0.134
0.063
2.13
0.03
*
Northeast
0.015
0.085
0.18
0.86
Constant
8.198
0.255
32.21
0.00
*
N = 726 Pseudo R2 = 0.6234
Notes:
An * indicates significance at the 0.10 level.
P-values are based on Wald statistics.
Values are rounded. More precise coefficient values are posted at http://www.fcc.gov/encyclopedia/rate-
return-resources.
44

Federal Communications Commission

DA 12-646

Figure 1

Opex vs. Loops

30
25
20
15
10

Opex (millions)

5
0
0
10000
20000
30000
40000
50000
60000
70000
80000

Loops

Figure 2

Natural Log of Opex vs. Natural Log of Loops

18
16
14
12
10
8
6

Natural Log of Opex

4
2
0
0
2
4
6
8
10
12

Natural Log of Loops

45

Federal Communications Commission

DA 12-646

Figure 3

Opex per Loop vs. Density

12000
10000
8000
6000
4000

Opex per Loop

2000
0
0
200
400
600
800
1000
1200

Density

Figure 4

Natural Log of Opex vs. Natural Log of Density

18
16
14
12
10
8
6

Natural Log of Opex

4
2
0
-6
-4
-2
0
2
4
6
8

Natural Log of Density

46

Federal Communications Commission

DA 12-646

APPENDIX B

Quantile Regression Cost Per Loop (CPL)

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

3-RIVERS TEL COOP
482255
MT
17,970
$905
$490
$536
$416
$426
$905
ACCIPITER DBA ZONA
452191
AZ
520
$6,707
$4,134
$3,810
$2,574
$3,202
$6,383
ACE TEL ASSN-IA
351346
IA
3,997
$534
$225
$386
$308
$631
$534
ACE TEL ASSN-MN
361346
MN
9,833
$688
$350
$913
$338
$590
$688
ACE TEL OF MICHIGAN
310704
MI
4,370
$673
$303
$591
$369
$583
$673
ADAK TEL UTILITY
610989
AK
147
$12,739
$3,265
$3,265
$9,474
$9,474
$12,739
AGATE MUTUAL TEL CO
462178
CO
113
$4,530
$1,457
$4,109
$3,073
$3,549
$4,530
ALASKA TEL CO
613017
AK
3,737
$815
$262
$630
$552
$1,415
$815
ALBANY MUTUAL ASSN
361347
MN
3,336
$825
$490
$944
$335
$630
$825
ALBION TEL CO-ATC
472213
ID
3,853
$1,374
$537
$881
$837
$889
$1,374
ALENCO COMMUNICATION
442090
TX
1,888
$2,129
$881
$1,345
$1,248
$1,893
$2,129
ALHAMBRA-GRANTFORK
340978
IL
1,042
$657
$270
$593
$387
$1,070
$657
ALL WEST COMM.-WY
512290
WY
293
$906
$437
$823
$468
$1,584
$906
ALL WEST COMM-UT
502288
UT
4,572
$783
$457
$454
$326
$525
$780
ALLBAND COMM COOP
310542
MI
163
$8,283
$4,945
$5,972
$3,338
$3,338
$8,283
ALLENDALE TEL CO
310669
MI
3,842
$558
$294
$310
$265
$531
$558
ALLIANCE-SPLITROCK
391657
SD
7,212
$785
$504
$744
$280
$539
$785
ALMA COMM. CO.
421860
MO
342
$2,186
$1,093
$2,280
$1,093
$2,141
$2,186
ALMA TEL CO
220344
GA
6,090
$426
$84
$337
$342
$678
$426
ALPINE COMM.
351106
IA
5,168
$630
$313
$505
$317
$576
$630
AMELIA TEL CORP
190217
VA
5,095
$533
$226
$253
$307
$389
$533
AMERICAN SAMOA
673900
AS
9,884
$410
$161
$933
$249
$1,728
$410
ARAPAHOE TEL CO
371516
NE
1,989
$1,102
$379
$637
$723
$1,028
$1,102
ARCTIC SLOPE TEL
613001
AK
2,518
$1,417
$341
$341
$1,076
$877
$1,218
ARDMORE TEL CO
290280
TN
7,745
$434
$234
$307
$199
$495
$434
ARIZONA TELEPHONE CO
452171
AZ
2,967
$622
$296
$642
$326
$1,257
$622
ARKANSAS TEL CO
401692
AR
6,938
$379
$160
$335
$219
$547
$379
47

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

ARKWEST COMM., INC.
401734
AR
4,384
$972
$392
$581
$580
$813
$972
ARLINGTON TEL CO
371517
NE
927
$805
$250
$395
$555
$770
$805
ARMSTRONG OF WV
200256
WV
2,702
$632
$174
$381
$457
$565
$632
ARMSTRONG TEL CO-NY
150071
NY
2,800
$754
$240
$308
$515
$601
$754
ARMSTRONG TEL CO-PA
170189
PA
1,441
$973
$351
$261
$622
$665
$882
ARMSTRONG TEL OF MD
180216
MD
5,905
$546
$174
$281
$371
$469
$546
ARMSTRONG TEL. CO.
200267
WV
4,423
$748
$266
$422
$482
$573
$748
ARROWHEAD COMM CORP
361374
MN
569
$668
$311
$311
$357
$857
$668
ARVIG TEL CO
361350
MN
11,482
$526
$193
$304
$333
$312
$505
ASOTIN TEL - OR
532404
OR
120
$901
$160
$712
$740
$2,639
$901
ASOTIN TEL - WA
522404
WA
1,157
$729
$379
$547
$350
$1,014
$729
ATLANTIC MEMBERSHIP
230468
NC
37,985
$390
$186
$480
$204
$373
$390
ATLAS TEL CO
431966
OK
1,147
$864
$305
$574
$559
$1,311
$864
AYRSHIRE FARMERS MUT
351105
IA
254
$1,353
$676
$1,015
$677
$1,910
$1,353
BACA VALLEY TEL CO
492259
NM
662
$2,959
$1,381
$1,308
$1,577
$1,920
$2,885
BADGER TELECOM, INC.
330844
WI
5,275
$584
$229
$245
$356
$344
$573
BALLARD RURAL COOP
260396
KY
5,273
$756
$421
$759
$334
$747
$756
BARNARDSVILLE TEL CO
230469
NC
1,094
$772
$346
$411
$426
$748
$772
BAY SPRINGS TEL CO
280446
MS
9,000
$992
$415
$463
$577
$632
$992
BEAR LAKE COMM
503032
UT
784
$906
$283
$400
$623
$782
$906
BEAVER CREEK COOP
532359
OR
3,652
$614
$220
$505
$394
$728
$614
BEEHIVE TEL CO - NV
552284
NV
124
$2,615
$992
$1,832
$1,623
$4,061
$2,615
BEEHIVE TEL CO - UT
502284
UT
930
$3,026
$1,797
$2,969
$1,228
$2,554
$3,026
BEK COMM. COOP.
381604
ND
6,381
$1,337
$933
$1,206
$403
$784
$1,337
BENKELMAN TEL CO
372455
NE
1,175
$1,717
$753
$755
$964
$1,056
$1,717
BERNARD TEL CO INC
351110
IA
472
$1,380
$680
$1,049
$700
$1,416
$1,380
BETTLES TEL CO INC
613002
AK
208
$447
$156
$306
$290
$1,542
$447
BIG BEND TEL CO INC
442039
TX
5,602
$3,648
$1,643
$1,920
$2,006
$2,006
$3,648
BIJOU TEL COOP ASSOC
462181
CO
1,151
$1,289
$441
$1,064
$847
$1,055
$1,289
BIXBY TEL CO
431969
OK
6,908
$1,145
$447
$451
$698
$698
$1,145
BLACK EARTH TEL CO
330849
WI
1,168
$707
$293
$466
$414
$692
$707
48

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

BLACKFOOT TEL - BTC
482235
MT
6,765
$989
$485
$826
$504
$631
$989
BLACKFOOT TEL - CFT
483308
MT
8,084
$806
$417
$841
$389
$609
$806
BLANCA TEL CO
462182
CO
986
$2,675
$1,043
$1,320
$1,632
$1,304
$2,347
BLOOMER TEL CO
330850
WI
2,950
$840
$522
$553
$318
$593
$840
BLOOMINGDALE HOME
320742
IN
498
$1,433
$206
$902
$1,227
$1,337
$1,433
BLOOMINGDALE TEL CO
310679
MI
1,524
$720
$215
$377
$505
$687
$720
BLOSSOM TEL CO
442038
TX
984
$1,304
$671
$581
$633
$1,143
$1,214
BLOUNTSVILLE TEL CO
250282
AL
3,104
$626
$192
$245
$434
$546
$626
BLUE EARTH VALLEY
361358
MN
5,604
$600
$196
$431
$404
$525
$600
BLUE RIDGE TEL CO
220346
GA
10,315
$772
$364
$480
$408
$478
$772
BLUE VALLEY TELE-COM
411746
KS
2,662
$3,417
$1,999
$1,512
$1,419
$1,203
$2,714
BLUFFTON TEL. CO.
240512
SC
21,067
$884
$373
$663
$511
$466
$839
BORDER TO BORDER
442073
TX
96
$15,868
$7,813
$5,207
$8,055
$6,972
$12,179
BPS Tel. Co.
420463
MO
2,919
$671
$244
$787
$427
$1,063
$671
BRANTLEY TEL CO
220347
GA
4,793
$1,306
$672
$940
$634
$914
$1,306
BRAZORIA TEL CO
442040
TX
4,600
$1,243
$627
$627
$616
$916
$1,243
BRAZOS TEL COOP INC
442041
TX
4,599
$708
$192
$731
$515
$1,160
$708
BRETTON WOODS TEL CO
120038
NH
643
$558
$148
$319
$410
$872
$558
BRIDGEWATER TEL CO
361362
MN
5,834
$814
$393
$393
$420
$421
$814
BRISTOL BAY TEL COOP
613003
AK
1,543
$1,269
$348
$636
$921
$2,047
$1,269
BRUCE TEL CO - MS
280447
MS
2,321
$859
$323
$391
$536
$791
$859
BRUCE TEL CO, INC
330855
WI
1,420
$464
$216
$328
$248
$614
$464
BUGGS ISLAND COOP
190219
VA
3,989
$518
$244
$428
$274
$633
$518
BULLOCH COUNTY RURAL
220348
GA
8,941
$748
$490
$625
$258
$657
$748
BUSH-TEL INC.
613004
AK
956
$1,052
$295
$387
$756
$1,429
$1,052
BUTLER TEL CO
250284
AL
6,549
$552
$214
$329
$338
$601
$552
CALAVERAS TEL CO
542301
CA
3,929
$1,360
$609
$731
$751
$1,020
$1,360
CALHOUN CITY TEL CO
280448
MS
2,839
$407
$59
$240
$348
$678
$407
CAL-ORE TELEPHONE CO
542311
CA
2,139
$1,021
$379
$785
$642
$1,112
$1,021
CAMBRIDGE TEL CO
472215
ID
1,788
$1,277
$752
$1,184
$525
$1,164
$1,277
CAMBRIDGE TEL CO -NE
371526
NE
1,148
$1,611
$630
$1,820
$981
$1,397
$1,611
49

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

CAMDEN TEL & TEL CO
220351
GA
17,840
$480
$232
$354
$248
$454
$480
CAMERON TEL CO - LA
270425
LA
5,440
$1,457
$481
$574
$976
$976
$1,457
CAMERON TEL CO TEXAS
440425
TX
608
$1,196
$353
$705
$844
$1,840
$1,196
CAMPTI-PLEASANT HILL
270426
LA
2,058
$1,012
$367
$537
$645
$1,065
$1,012
CANADIAN VALLEY TEL
431974
OK
1,120
$1,359
$489
$579
$870
$1,390
$1,359
CANBY TEL ASSN
532362
OR
9,571
$514
$217
$431
$297
$534
$514
CAP ROCK TEL COOP
442046
TX
4,396
$786
$309
$1,121
$477
$1,317
$786
CARNEGIE TEL CO INC
431976
OK
1,268
$1,075
$323
$663
$752
$1,311
$1,075
CARR TEL CO
310683
MI
1,342
$630
$228
$547
$402
$821
$630
CASCADE UTIL INC
532371
OR
7,753
$614
$261
$656
$353
$717
$614
CASS TEL CO
340984
IL
2,061
$744
$120
$559
$624
$931
$744
CENTRAL ARKANSAS TEL
401697
AR
2,602
$931
$394
$456
$538
$738
$931
CENTRAL MONTANA
483310
MT
7,317
$1,289
$729
$944
$560
$603
$1,289
CENTRAL OKLAHOMA TEL
431977
OK
2,372
$1,616
$879
$1,097
$736
$1,385
$1,616
CENTRAL STATE TEL CO
330859
WI
8,371
$586
$244
$271
$342
$342
$586
CENTRAL TEXAS CO-OP
442052
TX
6,562
$1,471
$991
$695
$480
$1,063
$1,175
CENTRAL UTAH TEL INC
502277
UT
1,615
$846
$383
$809
$463
$981
$846
CHAMPLAIN TEL CO
150077
NY
4,227
$563
$130
$266
$433
$438
$563
CHARITON VALLEY TEL
421864
MO
6,415
$1,999
$855
$1,326
$1,144
$928
$1,783
CHATHAM TEL CO - MI
310685
MI
2,363
$604
$249
$422
$355
$668
$604
CHAZY & WESTPORT
150079
NY
2,959
$504
$207
$239
$297
$507
$504
CHEQUAMEGON COM COOP
330860
WI
8,914
$1,044
$625
$755
$419
$570
$1,044
CHEROKEE TEL CO
431979
OK
3,829
$760
$355
$965
$405
$1,222
$760
CHEYENNE RIVER SIOUX
391647
SD
3,053
$1,097
$590
$1,348
$507
$1,332
$1,097
CHIBARDUN TEL COOP
330861
WI
4,660
$1,277
$867
$709
$410
$588
$1,119
CHICKAMAUGA TEL CORP
220354
GA
5,106
$640
$288
$391
$352
$624
$640
CHICKASAW TEL CO
431980
OK
6,753
$1,518
$342
$610
$1,176
$962
$1,305
CHRISTENSEN COMM CO
361425
MN
1,300
$501
$97
$323
$404
$683
$501
CHUGWATER TEL CO
512289
WY
166
$1,254
$223
$803
$1,030
$1,929
$1,254
CHURCHILL-CC COMM.
552349
NV
10,295
$873
$437
$1,099
$436
$789
$873
CIMARRON TEL CO
431982
OK
6,538
$1,078
$438
$639
$640
$953
$1,078
50

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

CITIZENS HAMMOND NY
150081
NY
1,133
$1,093
$428
$428
$665
$870
$1,093
CITIZENS MUTUAL TEL
351129
IA
3,382
$959
$594
$950
$365
$851
$959
CITIZENS TEL CO
230473
NC
17,455
$703
$335
$392
$368
$378
$703
CITIZENS TEL CO - GA
220355
GA
3,574
$586
$162
$377
$424
$818
$586
CITIZENS TEL CO - MO
421865
MO
3,492
$840
$291
$844
$549
$861
$840
CITIZENS TEL COOP-WI
330863
WI
1,988
$1,383
$810
$943
$573
$755
$1,383
CLARA CITY TEL EXCH
361370
MN
1,349
$622
$186
$380
$435
$683
$622
CLARENCE TEL CO
351130
IA
641
$1,559
$1,121
$1,641
$438
$1,356
$1,559
CLARKS TELECOM CO.
371531
NE
716
$2,064
$1,304
$3,644
$760
$1,929
$2,064
CLAY DBA ENDEAVOR
320753
IN
10,136
$940
$519
$915
$421
$596
$940
CLEAR CREEK MUTUAL
532363
OR
2,930
$730
$244
$469
$487
$769
$730
CLEAR LAKE INDEPEND
351132
IA
4,883
$897
$458
$417
$439
$538
$856
CLEVELAND COUNTY TEL
401698
AR
2,702
$602
$225
$353
$378
$768
$602
COCHRANE COOP TEL CO
330866
WI
1,019
$2,018
$1,277
$2,011
$741
$1,327
$2,018
COLEMAN COUNTY CO-OP
442057
TX
1,896
$1,730
$911
$1,421
$818
$1,784
$1,730
COLO TEL CO
351134
IA
594
$1,878
$1,311
$1,420
$567
$1,340
$1,878
COLORADO VALLEY TEL
442059
TX
6,286
$946
$483
$550
$464
$839
$946
COLTON TEL CO
532364
OR
1,013
$1,367
$497
$953
$870
$1,245
$1,367
COLUMBUS TELEPHONE
411756
KS
1,793
$1,156
$545
$729
$611
$1,105
$1,156
COMM 1 NETWORK
351262
IA
535
$1,642
$1,079
$1,653
$563
$1,587
$1,642
COMM CORP OF INDIANA
320776
IN
9,644
$548
$261
$276
$287
$357
$548
COMM CORP OF MI
310672
MI
3,424
$498
$201
$294
$296
$528
$498
COMMUNITY TEL CO
442061
TX
1,470
$1,063
$276
$863
$788
$1,533
$1,063
COMSOUTH TELECOMM
220369
GA
4,075
$638
$212
$389
$426
$725
$638
CONCORD TEL EXCHANGE
290559
TN
14,991
$819
$451
$279
$368
$347
$626
CONNEAUT TEL CO
300606
OH
5,066
$745
$434
$478
$311
$647
$745
CONSOLIDATED TEL CO
371532
NE
2,576
$1,484
$502
$983
$983
$1,074
$1,484
CONSOLIDATED TELCOM
381607
ND
7,103
$1,312
$756
$1,041
$556
$788
$1,312
CONSOLIDATED TELECOM
371562
NE
940
$1,244
$344
$1,463
$900
$1,367
$1,244
CONTINENTAL OF OHIO
300607
OH
2,092
$385
$110
$273
$275
$634
$385
COOP TEL EXCHANGE
351303
IA
613
$1,442
$910
$1,864
$533
$1,546
$1,442
51

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

COPPER VALLEY TEL
613006
AK
4,538
$2,876
$1,627
$1,627
$1,250
$1,250
$2,876
CORDOVA TEL COOP
613007
AK
1,711
$1,821
$822
$896
$999
$2,345
$1,821
COUNCIL GROVE TEL CO
411758
KS
1,835
$2,352
$1,561
$2,079
$791
$1,175
$2,352
COZAD TEL CO
371534
NE
1,893
$1,268
$405
$432
$863
$808
$1,213
CRAW-KAN TEL COOP
411818
KS
11,291
$913
$530
$647
$382
$726
$913
CROCKETT TEL CO
290561
TN
3,191
$517
$213
$362
$304
$619
$517
CROSS TEL CO
431985
OK
7,613
$1,088
$425
$425
$664
$765
$1,088
CROWN POINT TEL CORP
150085
NY
827
$1,080
$344
$370
$736
$800
$1,080
CUMBY TEL COOP INC
442065
TX
736
$903
$305
$701
$598
$1,301
$903
CUNNINGHAM TEL CO
411761
KS
1,085
$2,279
$1,251
$1,371
$1,029
$1,652
$2,279
CURTIS TEL CO
371536
NE
593
$1,418
$464
$533
$953
$989
$1,418
CUSTER TEL COOP
472218
ID
2,312
$1,676
$1,004
$902
$672
$903
$1,574
DAKOTA CENTRAL COOP
381610
ND
4,187
$1,231
$712
$1,112
$520
$840
$1,231
DALTON TEL CO, INC
371537
NE
903
$1,626
$780
$719
$847
$1,258
$1,566
DARIEN TEL CO
220358
GA
4,878
$1,191
$411
$584
$780
$836
$1,191
DAVIESS-MARTIN/RTC
320759
IN
3,063
$1,300
$711
$1,074
$589
$941
$1,300
DECATUR TEL CO INC
401699
AR
884
$557
$203
$371
$355
$1,015
$557
DEERFIELD FARMERS
310691
MI
1,907
$1,137
$309
$452
$829
$754
$1,063
DEKALB TEL COOP
290562
TN
16,778
$526
$233
$535
$293
$517
$526
DELHI TEL CO
150088
NY
3,693
$712
$330
$386
$382
$454
$712
DELL TEL CO-OP - NM
492066
NM
497
$2,658
$1,196
$1,464
$1,462
$3,116
$2,658
DELL TEL. CO-OP - TX
442066
TX
794
$6,594
$3,729
$2,162
$2,864
$2,837
$4,999
DELTA COUNTY TEL CO
462184
CO
8,467
$499
$214
$392
$284
$491
$499
DELTA TEL CO
280452
MS
3,180
$816
$186
$496
$630
$881
$816
DEPOSIT TEL CO
150089
NY
6,775
$397
$122
$200
$275
$356
$397
DICKEY RURAL COOP
381611
ND
7,707
$1,136
$632
$1,215
$503
$762
$1,136
DILLER TEL CO
371540
NE
795
$1,932
$663
$1,065
$1,269
$1,466
$1,932
DIRECT COMM-ROCKLAND
472232
ID
1,068
$1,319
$602
$827
$717
$1,181
$1,319
DIRECTCOMM-CEDAR VAL
500758
UT
2,591
$1,108
$632
$1,613
$476
$1,008
$1,108
DOBSON TEL CO
431988
OK
3,464
$2,095
$915
$703
$1,180
$1,156
$1,859
DRENTHE TEL CO
310692
MI
590
$857
$454
$669
$402
$1,018
$857
52

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

DUBOIS TEL EXCHANGE
512291
WY
2,325
$1,182
$559
$819
$623
$942
$1,182
DUCOR TELEPHONE CO
542313
CA
1,157
$1,691
$680
$1,866
$1,010
$2,278
$1,691
DUNKIRK & FREDONIA
150091
NY
5,875
$247
$66
$149
$182
$399
$247
DUO COUNTY TEL COOP
260401
KY
11,327
$899
$438
$506
$461
$469
$899
EAGLE TEL SYSTEMS
532369
OR
425
$1,600
$484
$869
$1,116
$1,474
$1,600
EAGLE VALLEY TEL CO
361383
MN
607
$351
$76
$280
$276
$755
$351
EAST ASCENSION TEL
270429
LA
29,375
$1,334
$498
$330
$836
$438
$768
EAST BUCHANAN COOP
351156
IA
1,455
$750
$394
$539
$356
$876
$750
EAST OTTER TAIL TEL
361385
MN
15,320
$634
$325
$315
$309
$298
$614
EASTERN NEBRASKA TEL
371542
NE
2,757
$442
$137
$381
$305
$727
$442
EASTERN SLOPE RURAL
462186
CO
4,211
$861
$386
$650
$475
$771
$861
EASTEX TEL COOP INC
442068
TX
24,639
$977
$403
$628
$574
$588
$977
EASTON TEL CO
361384
MN
882
$1,125
$238
$585
$888
$900
$1,125
ECKLES TEL CO
361386
MN
3,854
$671
$226
$472
$445
$496
$671
EDWARDS TEL CO
150092
NY
1,801
$757
$285
$189
$472
$485
$662
EGYPTIAN COOP ASSN
341003
IL
2,848
$876
$356
$544
$521
$909
$876
ELECTRA TELEPHONE CO
442069
TX
1,250
$946
$195
$496
$751
$1,254
$946
ELIZABETH TEL CO
270430
LA
2,685
$1,774
$676
$963
$1,098
$1,096
$1,772
ELKHART TEL CO INC
411764
KS
1,361
$2,705
$664
$794
$2,041
$1,168
$1,832
ELLIJAY TEL CO
220360
GA
12,428
$768
$320
$420
$448
$470
$768
ELLINGTON TEL CO
421874
MO
1,853
$1,249
$464
$1,114
$785
$1,232
$1,249
ELSIE COMM., INC.
371518
NE
178
$1,660
$608
$699
$1,051
$1,805
$1,660
EMILY COOP TEL CO
361387
MN
1,223
$1,830
$1,250
$1,507
$580
$888
$1,830
EMPIRE TEL CORP
150093
NY
5,646
$439
$214
$321
$225
$510
$439
EMRY dba EMRY TELCOM
502278
UT
4,271
$486
$160
$693
$326
$942
$486
ENMR TEL COOP INC-NM
492262
NM
10,086
$1,205
$659
$856
$546
$899
$1,205
ENMR TEL COOP-TX
442262
TX
681
$647
$330
$920
$317
$1,632
$647
ETEX TEL COOP INC
442070
TX
12,099
$1,222
$461
$496
$760
$585
$1,046
ETS TEL. CO., INC.
442091
TX
12,974
$666
$386
$528
$279
$466
$666
FARMERS MUTUAL COOP
351169
IA
442
$1,272
$808
$1,888
$463
$1,680
$1,272
FARMERS MUTUAL TEL
300612
OH
422
$461
$80
$709
$381
$1,295
$461
53

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

FARMERS MUTUAL TEL
351172
IA
1,900
$1,195
$658
$912
$538
$923
$1,195
FARMERS MUTUAL TEL
351174
IA
950
$1,170
$505
$953
$665
$1,131
$1,170
FARMERS MUTUAL TEL
361389
MN
998
$1,570
$955
$2,210
$615
$1,419
$1,570
FARMERS MUTUAL TEL
472221
ID
2,912
$530
$230
$509
$300
$714
$530
FARMERS TEL CO - CO
462188
CO
488
$1,533
$668
$981
$866
$1,436
$1,533
FARMERS TEL COOP
240520
SC
44,895
$870
$457
$513
$414
$419
$870
FARMERS TELECOM COOP
250290
AL
14,819
$752
$337
$661
$415
$481
$752
FELTON TEL CO. INC.
361391
MN
598
$732
$362
$543
$370
$1,027
$732
FIDELITY TEL CO
421882
MO
13,552
$495
$187
$434
$308
$510
$495
FILER MUTUAL TEL -ID
472220
ID
1,687
$1,119
$567
$1,107
$553
$1,063
$1,119
FILER MUTUAL TEL -NV
552220
NV
537
$283
$152
$1,080
$131
$1,601
$283
FISHERS ISLAND TEL
150095
NY
983
$505
$104
$568
$401
$1,116
$505
FIVE AREA TEL CO-OP
442071
TX
5,317
$962
$482
$936
$480
$1,047
$962
FLAT ROCK TEL CO-OP
341012
IL
520
$425
$119
$494
$306
$1,313
$425
FOOTHILLS RURAL COOP
260406
KY
14,396
$1,016
$574
$741
$442
$486
$1,016
FORESTHILL-SEBASTIAN
542318
CA
2,801
$1,479
$626
$1,302
$854
$1,069
$1,479
FORT MILL TEL CO
240521
SC
21,384
$600
$260
$343
$340
$340
$600
FORT MOJAVE TEL, INC
452200
AZ
1,014
$1,370
$555
$1,093
$815
$2,185
$1,370
FRANKLIN TEL CO - MS
280454
MS
7,090
$1,301
$572
$581
$729
$759
$1,301
FULTON TEL CO
280455
MS
6,972
$618
$332
$457
$285
$558
$618
GANADO TELEPHONE CO
442076
TX
2,536
$1,496
$646
$1,228
$850
$1,400
$1,496
GARDEN VALLEY TEL CO
361395
MN
14,135
$682
$364
$842
$318
$529
$682
GEORGETOWN TEL CO
280456
MS
276
$3,081
$1,199
$621
$1,882
$1,882
$2,503
GERMANTOWN TEL CO
150097
NY
2,416
$497
$75
$255
$422
$524
$497
GERVAIS TELEPHONE CO
532373
OR
777
$1,037
$400
$1,003
$637
$1,686
$1,037
GILA RIVER TELECOM.
452179
AZ
3,641
$2,683
$1,343
$2,062
$1,339
$1,818
$2,683
GLENWOOD TEL CO
220365
GA
733
$1,342
$454
$656
$888
$1,434
$1,342
GLENWOOD TEL MEMBER
371553
NE
2,147
$1,829
$894
$845
$935
$945
$1,780
GOLDEN BELT TEL ASSN
411777
KS
5,059
$1,626
$871
$889
$755
$978
$1,626
GOLDEN WEST TELECOM
391659
SD
13,393
$1,205
$645
$963
$561
$710
$1,205
GOLDEN WEST-KADOKA
391667
SD
454
$565
$177
$553
$388
$1,313
$565
54

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

GOLDEN WEST-VIVIAN
391686
SD
15,972
$828
$517
$699
$311
$551
$828
GOODMAN TEL CO
421886
MO
1,494
$1,396
$704
$1,090
$692
$1,346
$1,396
GORHAM TEL CO
411778
KS
273
$2,876
$1,533
$3,123
$1,343
$3,170
$2,876
GRAFTON TEL CO
341020
IL
834
$552
$164
$649
$387
$1,110
$552
GRANADA TEL CO
361399
MN
172
$478
$158
$312
$320
$1,243
$478
GRANBY TEL & TEL -MA
110036
MA
2,277
$207
$89
$238
$118
$624
$207
GRANBY TEL CO - MO
421887
MO
2,151
$1,321
$577
$1,097
$744
$1,010
$1,321
GRAND RIVER MUT-IA
351888
IA
6,262
$429
$194
$498
$234
$686
$429
GRAND RIVER MUT-MO
421888
MO
12,335
$873
$532
$873
$342
$674
$873
GRAND TEL CO INC
431994
OK
3,251
$1,287
$612
$631
$674
$1,138
$1,287
GRANITE STATE TEL
120039
NH
8,006
$515
$227
$230
$288
$412
$515
GREAT PLAINS COMMUN
371577
NE
25,547
$591
$213
$673
$378
$527
$591
GREEN HILLS TEL CORP
421890
MO
3,231
$1,425
$783
$1,309
$642
$1,200
$1,425
GRIDLEY TEL CO
341023
IL
1,214
$1,073
$330
$542
$743
$888
$1,073
GRISWOLD CO-OP TEL
351195
IA
1,712
$690
$348
$578
$343
$887
$690
GTA TELECOM, LLC
663800
GU
48,142
$578
$338
$466
$240
$857
$578
GUADALUPE VALLEY TEL
442083
TX
37,936
$773
$469
$576
$305
$486
$773
H & B COMMUNICATIONS
411781
KS
816
$1,034
$429
$959
$605
$1,565
$1,034
HAMPDEN TEL CO
100010
ME
2,401
$526
$165
$202
$361
$428
$526
HANCOCK TEL CO
150099
NY
1,550
$526
$89
$268
$438
$641
$526
HANCOCK TELECOM
320775
IN
6,098
$1,385
$796
$708
$589
$604
$1,297
HAPPY VALLEY TEL CO
542321
CA
3,011
$469
$67
$309
$402
$787
$469
HARDY TELECOM
200259
WV
3,646
$665
$330
$439
$335
$545
$665
HARGRAY TEL CO
240523
SC
35,827
$490
$146
$216
$344
$344
$490
HARRISONVILLE TEL CO
341026
IL
16,334
$707
$304
$545
$403
$557
$707
HART TEL CO
220368
GA
7,045
$590
$104
$231
$486
$477
$581
HARTINGTON TEL CO
371556
NE
1,329
$1,337
$514
$1,146
$823
$1,015
$1,337
HARTLAND & ST ALBANS
100011
ME
3,183
$421
$89
$144
$332
$347
$421
HARTMAN TEL EXCH INC
371557
NE
463
$2,799
$808
$1,536
$1,991
$2,021
$2,799
HAT ISLAND TEL CO
522417
WA
75
$599
$204
$1,075
$394
$3,076
$599
HAVILAND TEL CO
411780
KS
3,179
$1,736
$812
$874
$924
$1,076
$1,736
55

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

HAXTUN TEL CO
462190
CO
1,347
$865
$228
$563
$636
$939
$865
HAYNEVILLE TEL CO
250299
AL
2,129
$665
$252
$720
$414
$1,221
$665
HEART OF IOWA COMM.
351297
IA
2,080
$1,913
$1,198
$1,198
$715
$1,105
$1,913
HEARTLND-HICKORYTECH
351096
IA
8,828
$255
$48
$258
$207
$462
$255
HELIX TEL CO.
532376
OR
253
$1,627
$551
$1,196
$1,076
$2,580
$1,627
HEMINGFORD COOP TEL
371558
NE
742
$2,159
$985
$984
$1,173
$1,282
$2,158
HENDERSON CO-OP TEL
371559
NE
876
$1,154
$526
$978
$629
$1,152
$1,154
HERSHEY COOP TEL CO
371561
NE
631
$1,123
$524
$696
$600
$1,127
$1,123
HIAWATHA TEL CO
310713
MI
5,310
$659
$230
$578
$428
$660
$659
HILL COUNTRY CO-OP
442086
TX
15,174
$1,050
$590
$976
$460
$934
$1,050
HILLSBORO TEL CO
330892
WI
1,468
$680
$324
$574
$356
$745
$680
HINTON TEL CO
431995
OK
2,898
$1,012
$406
$937
$606
$1,199
$1,012
HOLWAY TEL CO
421929
MO
456
$580
$188
$650
$392
$1,384
$580
HOME TEL CO
240527
SC
20,094
$558
$238
$387
$320
$499
$558
HOME TEL CO
411782
KS
1,712
$2,016
$663
$1,209
$1,352
$1,345
$2,008
HOME TEL CO-ST JACOB
341032
IL
1,017
$2,577
$763
$776
$1,814
$1,122
$1,885
HOME TELEPHONE CO
532377
OR
692
$451
$101
$476
$350
$1,366
$451
HOOD CANAL TEL CO
522419
WA
1,004
$1,045
$208
$288
$837
$966
$1,045
HOPI TELECOMM, INC.
450815
AZ
1,731
$985
$438
$1,463
$547
$1,702
$985
HOPPER TELECOMM. CO.
250300
AL
2,980
$1,351
$463
$316
$889
$610
$927
HORNITOS TEL CO
542322
CA
592
$654
$184
$665
$470
$1,772
$654
HORRY TEL COOP
240528
SC
71,027
$506
$315
$366
$192
$323
$506
HOT SPRINGS TEL CO
482241
MT
874
$935
$140
$693
$796
$1,252
$935
HUMBOLDT TEL CO
553304
NV
966
$2,181
$927
$2,108
$1,254
$2,054
$2,181
HUMPHREY'S COUNTY
290566
TN
1,534
$449
$162
$307
$287
$654
$449
IAMO TEL CO - IA
351206
IA
334
$514
$195
$619
$319
$1,611
$514
IAMO TEL CO - MO
421206
MO
853
$614
$222
$727
$392
$1,332
$614
INDIANHEAD TEL CO
330936
WI
1,997
$522
$249
$280
$273
$553
$522
INDUSTRY TEL CO
442093
TX
2,275
$1,178
$547
$798
$631
$1,086
$1,178
INLAND TEL CO -WA
522423
WA
2,484
$898
$260
$764
$638
$982
$898
INLAND TEL-ID
472423
ID
346
$1,394
$422
$1,034
$972
$2,343
$1,394
56

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

INTERBEL TEL COOP
482242
MT
2,331
$1,614
$853
$861
$761
$750
$1,603
INTER-COMMUNITY TEL
381616
ND
2,231
$751
$199
$449
$552
$806
$751
INTERIOR TEL CO INC
613011
AK
4,404
$1,087
$256
$299
$830
$1,264
$1,087
INTERSTATE 35 TEL CO
351209
IA
1,075
$1,277
$675
$1,013
$602
$1,041
$1,277
INTERSTATE TELECOMM.
391654
SD
12,549
$826
$507
$791
$319
$539
$826
ISLAND TEL CO
100007
ME
635
$458
$119
$329
$339
$1,130
$458
ISLAND TEL CO
310677
MI
1,153
$470
$193
$353
$278
$742
$470
ITS TELECOMM. SYS.
210331
FL
2,980
$1,414
$332
$691
$1,083
$1,180
$1,414
J. B. N. TEL CO INC
411785
KS
2,141
$891
$294
$505
$597
$1,012
$891
JEFFERSON TEL CO -SD
391666
SD
414
$1,494
$181
$491
$1,313
$1,147
$1,328
JOHNSON TEL CO
361410
MN
1,771
$1,041
$483
$582
$558
$793
$1,041
K & M TEL CO, INC
371565
NE
599
$910
$238
$687
$672
$1,274
$910
KALAMA TEL CO
522426
WA
2,667
$841
$352
$340
$489
$655
$829
KALONA COOP TEL CO
351214
IA
1,824
$891
$478
$985
$413
$860
$891
KANOKLA TEL ASSN-KS
411788
KS
1,837
$2,830
$1,494
$1,123
$1,336
$1,405
$2,458
KANOKLA TEL ASSN-OK
431788
OK
1,003
$2,671
$1,554
$1,919
$1,117
$1,916
$2,671
KAPLAN TEL CO
270432
LA
3,768
$956
$324
$711
$632
$1,096
$956
KASSON & MANTORVILLE
361412
MN
4,027
$652
$343
$723
$309
$680
$652
KEARSARGE TEL CO
120045
NH
7,481
$476
$170
$177
$306
$366
$476
KENNEBEC TEL CO
391668
SD
734
$2,258
$872
$1,064
$1,386
$1,385
$2,258
KERMAN TEL-SEBASTIAN
542324
CA
6,002
$1,061
$364
$645
$697
$718
$1,061
KETCHIKAN PUBLIC UT
613013
AK
6,790
$958
$289
$423
$668
$1,698
$958
KEYSTONE-ARTHUR TEL
371567
NE
445
$1,773
$286
$476
$1,488
$1,357
$1,643
KINGDOM TELEPHONE CO
421901
MO
4,873
$824
$454
$824
$371
$756
$824
KNOLOGY - VALLEY
220371
GA
8,984
$262
$51
$402
$210
$778
$262
KNOLOGY COMM TEL
391652
SD
4,393
$589
$223
$387
$366
$638
$589
KNOLOGY TOTAL COMM
250295
AL
3,591
$622
$89
$412
$533
$863
$622
LA HARPE TEL CO
341043
IL
828
$1,681
$658
$824
$1,023
$1,240
$1,681
LA HARPE TEL CO INC
411791
KS
318
$3,912
$1,416
$2,426
$2,496
$2,356
$3,771
LA JICARITA RURAL
492263
NM
1,979
$1,360
$543
$672
$817
$938
$1,360
LA VALLE TEL COOP
330899
WI
1,599
$987
$444
$687
$543
$827
$987
57

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

LA WARD TEL EXCHANGE
442103
TX
884
$1,607
$600
$1,370
$1,007
$1,983
$1,607
LACKAWAXEN TELECOM
170177
PA
2,730
$346
$59
$209
$286
$523
$346
LAFOURCHE TEL CO
270433
LA
10,312
$481
$50
$326
$431
$849
$481
LAKE LIVINGSTON TEL
442104
TX
816
$2,412
$695
$823
$1,716
$1,529
$2,224
LAKESIDE TEL. CO.
280457
MS
274
$1,759
$60
$442
$1,698
$1,698
$1,759
LANCASTER TEL CO
240531
SC
18,327
$449
$100
$279
$349
$409
$449
LAVACA TEL CO-AR
401704
AR
1,194
$1,793
$751
$974
$1,042
$1,060
$1,793
LAVACA TEL CO-OK
431704
OK
1,076
$1,567
$659
$919
$908
$1,433
$1,567
LEACO RURAL TEL COOP
492264
NM
1,711
$2,204
$1,096
$1,266
$1,109
$1,604
$2,204
LEAF RIVER TEL CO
341045
IL
401
$1,929
$736
$807
$1,192
$1,333
$1,929
LEMONWEIR VALLEY TEL
330900
WI
2,840
$1,105
$626
$635
$479
$651
$1,105
LENNON TEL CO
310708
MI
925
$1,059
$174
$390
$885
$957
$1,059
LE-RU TELEPHONE CO
421908
MO
1,409
$1,558
$651
$656
$907
$1,221
$1,558
LESLIE COUNTY TEL CO
260411
KY
8,282
$567
$177
$253
$390
$434
$567
LEWIS RIVER TEL CO
522427
WA
5,232
$483
$164
$434
$319
$690
$483
LIGONIER TEL CO
320783
IN
1,540
$1,345
$539
$1,267
$806
$1,125
$1,345
LINCOLN CTY TEL SYS
552351
NV
2,360
$603
$293
$1,441
$310
$1,483
$603
LINCOLN TEL CO INC
482244
MT
983
$637
$259
$574
$379
$885
$637
LINCOLNVILLE NETWRKS
100003
ME
11,486
$272
$80
$216
$192
$384
$272
LIPAN TEL CO
442105
TX
1,435
$1,815
$753
$953
$1,062
$1,764
$1,815
LISMORE COOP TEL CO
361419
MN
312
$1,293
$808
$3,583
$485
$2,096
$1,293
LITTLE MIAMI COMM.
300613
OH
1,961
$597
$247
$312
$350
$632
$597
LOGAN TEL. COOP. INC
260413
KY
5,783
$1,071
$657
$688
$414
$624
$1,071
LONSDALE TEL CO
361422
MN
1,567
$1,571
$992
$906
$579
$777
$1,484
LOST NATION-ELWOOD
351229
IA
555
$1,728
$775
$1,556
$953
$1,496
$1,728
LUCK TEL CO
330902
WI
1,931
$793
$357
$485
$436
$599
$793
LUDLOW TEL CO
140058
VT
4,100
$367
$130
$130
$238
$338
$367
MADISON COUNTY TEL
401709
AR
3,418
$879
$286
$473
$593
$765
$879
MADISON TEL CO
341049
IL
1,455
$1,589
$302
$621
$1,287
$1,057
$1,359
MADISON TEL., LLC
411801
KS
546
$1,644
$532
$576
$1,112
$1,603
$1,644
MAHANOY & MAHANTANGO
170183
PA
3,252
$459
$166
$178
$293
$525
$459
58

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

MARGARETVILLE TEL CO
150104
NY
3,378
$332
$109
$185
$223
$445
$332
MARK TWAIN RURAL TEL
421914
MO
3,709
$1,087
$549
$581
$538
$832
$1,087
MARQUETTE-ADAMS COOP
330908
WI
3,268
$1,480
$957
$830
$523
$697
$1,353
MASHELL TELECOM INC
522431
WA
3,329
$766
$125
$310
$642
$661
$766
MATANUSKA TEL ASSOC
613015
AK
46,802
$821
$430
$327
$391
$506
$719
MCCLELLANVILLE TEL
240533
SC
1,478
$996
$337
$443
$659
$1,034
$996
MCCLURE TEL CO
300598
OH
582
$1,974
$879
$1,368
$1,095
$1,417
$1,974
MCDONALD COUNTY TEL
421912
MO
3,529
$1,239
$511
$756
$728
$1,046
$1,239
MCDONOUGH TEL COOP
341047
IL
3,592
$1,324
$608
$907
$716
$906
$1,324
MCLOUD TEL CO
432006
OK
7,038
$1,088
$422
$422
$666
$666
$1,088
MCNABB TEL CO
341048
IL
386
$476
$127
$491
$349
$1,289
$476
MCTA, INC.
123321
NH
9,131
$377
$145
$151
$232
$311
$377
MEDICINE PARK TEL CO
432008
OK
676
$2,078
$646
$1,479
$1,432
$2,295
$2,078
MERCHANTS & FARMERS
320788
IN
415
$711
$176
$429
$535
$1,047
$711
MERRIMACK COUNTY TEL
120047
NH
6,499
$404
$150
$193
$254
$392
$404
MESCALERO APACHE
491231
NM
1,151
$2,908
$1,002
$1,081
$1,906
$1,809
$2,811
MID CENTURY TEL COOP
341054
IL
3,977
$741
$458
$1,049
$283
$959
$741
MID MAINE TELECOM
103315
ME
4,185
$498
$156
$121
$342
$339
$460
MID STATE TEL CO
361433
MN
5,827
$503
$229
$363
$274
$460
$503
MID-AMERICA TEL INC
432010
OK
1,261
$849
$418
$609
$431
$1,542
$849
MIDDLEBURGH TEL CO
150105
NY
5,848
$284
$69
$207
$215
$422
$284
MID-MISSOURI TEL CO
421917
MO
3,437
$1,296
$498
$583
$798
$897
$1,296
MID-PLAINS RURAL TEL
442112
TX
2,796
$1,071
$474
$1,324
$597
$1,396
$1,071
MID-RIVERS TEL COOP
482246
MT
10,042
$774
$359
$527
$415
$559
$774
MIDSTATE COMM., INC.
391670
SD
4,315
$959
$664
$1,314
$295
$952
$959
MIDSTATE TEL CO
381617
ND
1,870
$1,018
$419
$876
$599
$826
$1,018
MIDVALE TEL EXCH INC
472226
ID
957
$1,609
$538
$1,116
$1,071
$1,472
$1,609
MIDVALE-AZ
452226
AZ
1,226
$3,118
$1,377
$2,170
$1,740
$1,976
$3,118
MIDWAY TEL CO
310711
MI
703
$998
$344
$813
$654
$1,194
$998
MIDWAY TEL CO
330909
WI
7,154
$444
$152
$220
$292
$291
$443
MILLER TEL CO - MO
421920
MO
819
$991
$271
$524
$720
$1,131
$991
59

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

MILLINGTON TEL CO
290571
TN
20,820
$435
$144
$353
$292
$430
$435
MILLRY TEL CO
250304
AL
6,056
$724
$256
$513
$468
$712
$724
MILLTOWN MUTUAL TEL
330910
WI
2,137
$931
$390
$481
$541
$567
$931
MINBURN TELECOMM.
351158
IA
701
$777
$367
$886
$410
$1,091
$777
MOAPA VALLEY TEL CO.
552353
NV
3,302
$374
$141
$1,011
$233
$1,528
$374
MOKAN DIAL INC-KS
411807
KS
3,227
$949
$300
$431
$649
$762
$949
MOKAN DIAL INC-MO
421807
MO
688
$961
$366
$577
$595
$1,109
$961
MOLALLA TEL CO.
532383
OR
4,822
$1,192
$586
$858
$605
$792
$1,192
MON-CRE TEL COOP
250305
AL
2,445
$1,143
$508
$465
$635
$760
$1,100
MONITOR COOP TEL
532384
OR
555
$1,478
$593
$910
$885
$1,566
$1,478
MONON TEL CO
320790
IN
947
$1,556
$544
$729
$1,011
$1,101
$1,556
MONROE TELEPHONE CO.
532385
OR
884
$1,215
$473
$793
$741
$1,375
$1,215
MONTROSE MUTUAL TEL
341058
IL
1,423
$479
$113
$581
$365
$1,054
$479
MOULTRIE INDEPENDENT
341060
IL
569
$710
$149
$650
$561
$1,314
$710
MOUND BAYOU TEL & CO
280462
MS
690
$1,083
$559
$1,475
$524
$1,910
$1,083
MOUNDRIDGE TEL CO
411808
KS
2,417
$1,078
$484
$484
$595
$824
$1,078
MOUNDVILLE TEL CO
250307
AL
1,346
$888
$394
$695
$494
$1,082
$888
MOUNT HOREB TEL CO
330916
WI
3,728
$602
$324
$247
$278
$541
$525
MOUNTAIN RURAL COOP
260414
KY
14,989
$557
$279
$769
$278
$493
$557
MT VERNON TEL CO
330917
WI
10,537
$682
$264
$368
$418
$357
$621
MUENSTER DBA NORTEX
442116
TX
3,826
$1,264
$594
$731
$671
$976
$1,264
MUKLUK TEL CO INC
613016
AK
1,361
$1,044
$217
$507
$828
$1,273
$1,044
MUTUAL TEL CO
351252
IA
4,218
$651
$396
$632
$255
$548
$651
MUTUAL TEL CO
411809
KS
437
$3,778
$1,657
$2,302
$2,120
$2,199
$3,778
NATIONAL OF ALABAMA
250286
AL
1,665
$813
$343
$583
$470
$1,048
$813
NE MISSOURI RURAL
421931
MO
6,843
$1,102
$648
$789
$454
$733
$1,102
NEBRASKA CENTRAL TEL
371574
NE
6,319
$667
$299
$583
$368
$732
$667
NEHALEM TELECOMM.
532387
OR
2,814
$520
$168
$620
$352
$869
$520
NELSON TEL COOP
330918
WI
3,691
$1,170
$718
$684
$451
$644
$1,135
NELSON-BALL GROUND
220375
GA
6,690
$553
$233
$390
$319
$545
$553
NEMONT TEL COOP - ND
382247
ND
212
$3,294
$2,733
$4,638
$561
$3,370
$3,294
60

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

NEMONT TEL COOP-MT
482247
MT
11,196
$1,037
$452
$888
$585
$613
$1,037
NEW CASTLE TEL. CO.
193029
VA
2,110
$500
$203
$357
$297
$684
$500
NEW FLORENCE TEL CO
421927
MO
387
$1,035
$160
$580
$875
$1,453
$1,035
NEW HOPE TEL COOP
250308
AL
4,960
$1,187
$777
$1,056
$411
$806
$1,187
NEW LONDON TEL CO
421928
MO
747
$563
$198
$435
$365
$978
$563
NEW PARIS TEL INC
320797
IN
1,727
$645
$117
$358
$529
$713
$645
NEW ULM TELECOM, INC
361442
MN
10,727
$390
$159
$218
$232
$336
$390
NEWPORT TEL CO
150107
NY
2,987
$478
$165
$220
$313
$464
$478
NIAGARA TEL CO
330920
WI
3,601
$704
$298
$399
$406
$547
$704
NICHOLVILLE TEL CO
150108
NY
1,590
$648
$131
$221
$517
$525
$648
NORTH ARKANSAS TEL
401713
AR
6,111
$917
$382
$390
$535
$693
$917
NORTH CENTRAL COOP
290573
TN
19,553
$794
$456
$636
$337
$449
$794
NORTH DAKOTA TEL CO
381447
ND
13,946
$640
$306
$529
$334
$521
$640
NORTH PENN TEL CO
170192
PA
4,900
$685
$263
$285
$421
$487
$685
NORTH STATE TEL CO.
532388
OR
473
$4,196
$2,279
$2,718
$1,917
$2,211
$4,196
NORTHEAST FLORIDA
210335
FL
7,424
$748
$187
$359
$561
$633
$748
NORTHEAST LOUISIANA
270435
LA
643
$1,525
$523
$1,102
$1,002
$1,793
$1,525
NORTHEAST NEBRASKA
371576
NE
6,126
$772
$509
$1,311
$263
$853
$772
NORTHERN TEL COOP
482248
MT
1,536
$1,173
$565
$781
$608
$1,052
$1,173
NORTHFIELD TEL CO
140061
VT
2,436
$334
$99
$111
$235
$398
$334
NORTHWESTERN INDIANA
320800
IN
9,877
$456
$142
$302
$314
$463
$456
NOXAPATER TEL CO
280461
MS
776
$1,398
$130
$431
$1,268
$1,124
$1,254
NUCLA-NATURITA TEL
462193
CO
1,589
$773
$352
$942
$421
$1,297
$773
NUNN TEL CO
462194
CO
559
$3,140
$1,718
$1,561
$1,422
$1,360
$2,920
NUSHAGAK ELEC & TEL
613018
AK
2,114
$1,157
$365
$426
$792
$1,416
$1,157
OGDEN TEL CO
310714
MI
320
$977
$325
$695
$652
$1,445
$977
OKLAHOMA COMM SYSTEM
431984
OK
13,988
$605
$244
$265
$361
$513
$605
OKLAHOMA TEL & TEL
432013
OK
1,498
$1,362
$293
$632
$1,069
$1,415
$1,362
OKLAHOMA WESTERN TEL
432014
OK
2,577
$681
$329
$492
$352
$1,182
$681
ONEIDA COUNTY RURAL
150111
NY
2,315
$434
$177
$241
$257
$487
$434
ONEIDA TEL EXCHANGE
341066
IL
472
$469
$154
$680
$315
$1,287
$469
61

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

ONTARIO TEL CO, INC.
150112
NY
2,628
$790
$292
$475
$499
$688
$790
ONTONAGON COUNTY TEL
310717
MI
3,325
$561
$187
$569
$374
$660
$561
ORCHARD FARM TEL CO
421934
MO
641
$478
$177
$428
$301
$1,015
$478
OREGON FARMERS MUT
421935
MO
1,019
$662
$207
$483
$455
$893
$662
OREGON TEL CORP
532389
OR
1,558
$2,434
$1,418
$2,423
$1,016
$1,499
$2,434
OREGON-IDAHO UTIL.
532390
OR
638
$2,986
$1,265
$1,646
$1,721
$2,083
$2,986
ORISKANY FALLS TEL
150114
NY
434
$580
$211
$492
$370
$898
$580
OSAKIS TEL CO
361448
MN
1,486
$812
$504
$498
$308
$554
$806
OTZ TEL COOPERATIVE
613019
AK
2,950
$1,090
$409
$1,231
$681
$1,775
$1,090
OXFORD WEST TEL CO
100002
ME
5,734
$467
$95
$190
$372
$372
$467
Ozark Tel. Co.
421866
MO
2,127
$1,103
$560
$718
$544
$1,090
$1,103
PANHANDLE TEL COOP
432016
OK
13,384
$1,118
$670
$722
$448
$710
$1,118
PARTNER COMM. COOP.
351187
IA
891
$1,621
$858
$1,373
$763
$1,388
$1,621
PATTERSONVILLE TEL
150116
NY
905
$506
$117
$163
$388
$704
$506
PAUL BUNYAN RURAL
361451
MN
11,704
$1,005
$619
$810
$386
$525
$1,005
PBT TELECOM, INC.
240539
SC
12,672
$1,052
$475
$443
$577
$509
$951
PEETZ COOP TEL CO
462196
CO
227
$1,547
$614
$907
$933
$1,910
$1,547
PEMBROKE TEL CO
220376
GA
3,334
$1,079
$433
$577
$646
$850
$1,079
PENASCO VALLEY TEL
492270
NM
2,916
$2,075
$1,201
$1,388
$874
$1,586
$2,075
PEND OREILLE TEL.
522418
WA
1,827
$675
$114
$367
$561
$815
$675
PENINSULA TEL CO -MI
310720
MI
1,112
$350
$75
$245
$275
$747
$350
PEOPLES RURAL COOP
260415
KY
7,700
$1,011
$445
$821
$566
$555
$1,000
PEOPLES TEL CO
250314
AL
12,413
$728
$279
$372
$449
$472
$728
PEOPLES TEL CO
290576
TN
4,425
$658
$336
$407
$322
$594
$658
PEOPLES TEL CO - MN
361453
MN
1,689
$768
$372
$272
$396
$546
$668
PEOPLES TEL CO. - OR
532391
OR
1,092
$1,292
$562
$1,499
$730
$1,403
$1,292
PEOPLES TEL COOP -TX
442130
TX
11,701
$914
$399
$816
$515
$711
$914
PEOPLES TELECOM LLC
411814
KS
1,303
$1,841
$758
$630
$1,083
$1,031
$1,661
PERKINSVILLE TEL CO
140062
VT
801
$275
$67
$100
$208
$536
$275
PERRY-SPENCER RURAL
320807
IN
5,307
$888
$412
$885
$476
$826
$888
PHILLIPS COUNTY TEL
462197
CO
1,674
$1,451
$780
$1,337
$671
$973
$1,451
62

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

PIEDMONT RURAL COOP
240538
SC
10,708
$842
$483
$365
$359
$527
$724
PIGEON TEL CO
310721
MI
2,595
$1,018
$308
$457
$710
$699
$1,007
PINE BELT TEL CO
250315
AL
2,324
$1,294
$687
$981
$607
$1,079
$1,294
PINE ISLAND TEL CO
361454
MN
2,771
$590
$341
$325
$249
$492
$574
PINE TEL SYSTEM INC.
532392
OR
902
$4,411
$3,043
$2,203
$1,368
$1,519
$3,570
PINE TELEPHONE CO
432017
OK
4,998
$618
$283
$700
$335
$1,035
$618
PINELAND TEL COOP
220377
GA
10,502
$893
$656
$910
$237
$790
$893
PINNACLES TEL CO
542346
CA
253
$2,790
$904
$1,093
$1,886
$3,251
$2,790
PIONEER TEL ASSN INC
411817
KS
12,304
$808
$371
$444
$436
$651
$808
PIONEER TEL CO
522437
WA
725
$1,365
$831
$1,045
$534
$1,377
$1,365
PIONEER TEL COOP
532393
OR
12,644
$786
$392
$674
$394
$784
$786
PIONEER TEL COOP INC
432018
OK
46,095
$531
$212
$695
$318
$550
$531
PLAINS COOP TEL ASSN
462199
CO
1,222
$2,002
$816
$987
$1,186
$1,320
$2,002
PLAINVIEW TEL CO
371582
NE
979
$1,740
$796
$1,574
$944
$1,203
$1,740
PLANT TEL. CO.
220379
GA
7,268
$735
$288
$528
$447
$747
$735
PLANTERS RURAL COOP
220378
GA
7,450
$1,283
$850
$945
$434
$793
$1,283
POKA-LAMBRO TEL COOP
442131
TX
2,401
$974
$343
$903
$631
$1,479
$974
POLAR COMM MUT AID
381630
ND
7,758
$559
$280
$604
$279
$548
$559
PORT BYRON TEL CO
150118
NY
2,371
$537
$190
$250
$348
$550
$537
POTLATCH TEL CO INC
472230
ID
1,762
$478
$192
$399
$285
$823
$478
POTTAWATOMIE TEL CO
432020
OK
2,188
$1,516
$557
$595
$959
$1,200
$1,516
PRAIRIE GROVE TEL CO
401718
AR
8,086
$959
$430
$394
$529
$529
$922
PRICE COUNTY TEL CO
330937
WI
4,143
$456
$260
$283
$196
$416
$456
PROJECT MUTUAL TEL
472231
ID
5,871
$627
$295
$727
$332
$753
$627
PROJECT TEL CO
482250
MT
4,633
$1,036
$385
$884
$650
$782
$1,036
PUBLIC SERVICE TEL
220381
GA
9,097
$1,169
$481
$466
$688
$631
$1,097
PULASKI-WHITE RURAL
320813
IN
1,318
$985
$335
$669
$650
$955
$985
QUINCY TEL CO-FL DIV
210338
FL
10,326
$513
$202
$365
$311
$605
$513
QUINCY TEL CO-GA DIV
220338
GA
575
$646
$283
$530
$362
$1,326
$646
RADCLIFFE TEL CO
351277
IA
452
$950
$507
$1,269
$443
$1,437
$950
RAGLAND TEL CO
250316
AL
1,014
$1,759
$576
$715
$1,183
$1,217
$1,759
63

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

RAINBOW TELECOM
411820
KS
1,692
$3,021
$2,019
$2,323
$1,002
$1,581
$3,021
RANGE TEL COOP - WY
512251
WY
14,862
$908
$497
$516
$411
$514
$908
RANGE TEL COOP-MT
482251
MT
4,319
$738
$275
$494
$462
$696
$738
RED RIVER RURAL TEL
381631
ND
3,509
$1,056
$631
$1,230
$425
$862
$1,056
RESERVATION TEL COOP
381632
ND
6,629
$1,060
$519
$1,129
$541
$789
$1,060
RESERVE TEL CO
270438
LA
3,779
$562
$148
$298
$414
$909
$562
RICE BELT TEL CO
401721
AR
704
$1,615
$282
$1,523
$1,333
$1,886
$1,615
RICHLAND-GRANT COOP
330942
WI
2,342
$1,081
$620
$869
$461
$850
$1,081
RICHMOND TEL CO
110037
MA
939
$444
$80
$309
$365
$813
$444
RICO TEL CO
462201
CO
152
$569
$268
$1,741
$301
$2,346
$569
RINGGOLD TEL CO
220382
GA
10,089
$623
$289
$329
$334
$445
$623
RIO VIRGIN TEL CO
552356
NV
9,519
$436
$244
$852
$192
$794
$436
RIVIERA TEL CO INC
442134
TX
1,184
$1,988
$433
$1,492
$1,555
$2,325
$1,988
ROANOKE & BOTETOURT
190249
VA
8,498
$665
$282
$599
$383
$569
$665
ROANOKE TEL CO
250317
AL
4,033
$609
$239
$590
$370
$748
$609
ROBERTS COUNTY COOP
391674
SD
1,827
$1,049
$581
$748
$469
$1,123
$1,049
ROCHESTER TEL CO
320815
IN
5,625
$785
$406
$500
$378
$569
$785
ROCK COUNTY TEL CO
371586
NE
835
$460
$34
$369
$426
$947
$460
ROCK HILL TEL CO
240542
SC
39,493
$321
$105
$232
$216
$279
$321
ROGGEN TEL COOP CO
462202
CO
228
$1,985
$913
$1,223
$1,072
$1,709
$1,985
ROOME TELECOMM INC
532375
OR
527
$721
$214
$560
$506
$1,557
$721
ROOSEVELT CNTY RURAL
492272
NM
1,522
$1,427
$609
$1,370
$818
$1,625
$1,427
RURAL TEL CO - ID
472233
ID
684
$2,530
$1,088
$1,849
$1,442
$2,107
$2,530
RURAL TEL CO - NV
552233
NV
893
$1,267
$428
$1,110
$839
$1,511
$1,267
RURAL TEL SERVICE CO
411826
KS
8,164
$2,545
$893
$712
$1,652
$835
$1,546
RYE TELEPHONE CO
462203
CO
2,280
$1,799
$772
$970
$1,027
$1,179
$1,799
S & A TEL CO INC
411829
KS
702
$2,252
$805
$564
$1,447
$1,307
$1,871
S & T TEL COOP ASSN
411827
KS
2,455
$2,449
$1,087
$795
$1,363
$1,166
$1,961
S. CENTRAL TEL - KS
411831
KS
1,528
$2,627
$1,245
$1,341
$1,382
$1,586
$2,627
S. CENTRAL TEL - OK
431831
OK
297
$5,443
$1,818
$1,646
$3,625
$2,359
$4,004
SACRED WIND
493403
NM
2,600
$3,182
$1,877
$2,082
$1,306
$1,507
$3,182
64

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

SADDLEBACK COMM CO
457991
AZ
1,041
$2,554
$1,146
$2,948
$1,409
$2,877
$2,554
SALINA-SPAVINAW TEL
432022
OK
6,344
$619
$209
$367
$410
$826
$619
SALUDA MOUNTAIN TEL
230498
NC
1,461
$673
$297
$549
$375
$851
$673
SAN CARLOS APACHE
452169
AZ
2,529
$1,503
$820
$1,983
$683
$1,708
$1,503
SANDWICH ISLES COMM.
623021
HI
2,334
$9,278
$5,742
$2,263
$3,536
$3,536
$5,798
SANTA ROSA TEL COOP
442141
TX
1,872
$1,277
$497
$1,194
$780
$1,674
$1,277
SANTEL COMM. COOP.
391676
SD
4,447
$903
$551
$688
$352
$739
$903
SCIO MUTUAL TEL ASSN
532397
OR
1,598
$1,781
$1,117
$1,002
$663
$1,173
$1,665
SCOTT COUNTY COOP
190248
VA
5,952
$874
$393
$965
$481
$703
$874
SCOTT COUNTY TEL CO
403031
AR
130
$1,684
$429
$739
$1,256
$2,560
$1,684
SE INDIANA RURAL
320819
IN
4,157
$1,203
$597
$790
$606
$702
$1,203
SE NEBRASKA COMM INC
371591
NE
3,113
$1,029
$349
$544
$680
$825
$1,029
SE TEL OF WISCONSIN
330952
WI
5,724
$505
$208
$235
$297
$421
$505
SENECA TEL CO
421945
MO
2,796
$993
$499
$893
$494
$927
$993
SHAWNEE TEL. CO.
341025
IL
3,626
$1,804
$725
$1,100
$1,079
$1,045
$1,771
SHELL ROCK COMM
351295
IA
836
$685
$444
$1,302
$241
$1,128
$685
SHIAWASSEE TEL CO
310726
MI
4,440
$543
$227
$326
$317
$548
$543
SHIDLER TEL CO
432023
OK
728
$2,886
$1,575
$2,061
$1,311
$3,172
$2,886
SIERRA TELEPHONE CO
542338
CA
20,806
$842
$370
$400
$472
$487
$842
SILVER STAR TEL- ID
472295
ID
3,992
$1,749
$671
$513
$1,078
$609
$1,122
SILVER STAR TEL-WY
512295
WY
2,664
$1,239
$458
$604
$780
$701
$1,159
SIREN TEL CO, INC
330949
WI
2,249
$922
$476
$751
$446
$648
$922
SKYLINE TELECOM CO.
520581
WA
30
$12,290
$6,303
$6,592
$5,986
$8,034
$12,290
SKYLINE TELECOM, INC
521402
WA
140
$1,250
$359
$919
$891
$2,257
$1,250
SLEDGE TEL CO
280466
MS
369
$2,192
$1,288
$1,893
$905
$2,079
$2,192
SLEEPY EYE TEL CO
361483
MN
4,878
$421
$216
$282
$204
$465
$421
SMART CITY TEL LLC
210330
FL
9,751
$616
$186
$295
$430
$533
$616
SMITHVILLE COMM.
320818
IN
24,750
$1,377
$703
$835
$674
$486
$1,189
SOMERSET TEL CO
100024
ME
9,475
$446
$115
$164
$331
$318
$433
SOUTH ARKANSAS TEL
401702
AR
3,041
$1,024
$373
$517
$652
$893
$1,024
SOUTH CENTRAL RURAL
260418
KY
25,845
$802
$482
$442
$320
$404
$763
65

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

SOUTH CENTRAL UTAH
502286
UT
11,732
$614
$321
$618
$293
$658
$614
SOUTH PARK TEL. CO.
462195
CO
167
$6,116
$2,087
$1,197
$4,029
$2,160
$3,357
SOUTH PLAINS TEL
442143
TX
4,104
$760
$321
$871
$439
$1,053
$760
SOUTH SLOPE COOP TEL
351298
IA
9,956
$671
$394
$666
$277
$476
$671
SOUTHEAST MS TEL CO
283301
MS
2,997
$846
$386
$364
$460
$790
$824
SOUTHERN KANSAS TEL
411833
KS
3,986
$1,794
$746
$696
$1,048
$1,048
$1,744
SOUTHERN MONTANA TEL
482254
MT
947
$2,902
$1,692
$2,045
$1,210
$1,486
$2,902
SOUTHWEST TEXAS TEL
442135
TX
4,213
$1,439
$874
$1,225
$566
$1,536
$1,439
SOUTHWESTERN TEL CO
452174
AZ
3,302
$675
$307
$714
$368
$1,220
$675
SPRING GROVE COMM.
361485
MN
1,236
$1,152
$821
$2,007
$331
$1,108
$1,152
SPRING VALLEY TEL CO
330953
WI
1,065
$1,441
$816
$826
$626
$856
$1,441
SPRINGPORT TEL CO
310728
MI
1,380
$651
$220
$493
$431
$714
$651
SPRUCE KNOB SENECA
200257
WV
1,166
$1,537
$931
$1,268
$607
$970
$1,537
ST JOHN TEL CO
522442
WA
587
$3,411
$2,277
$2,362
$1,134
$1,717
$3,411
ST STEPHEN TEL CO
240544
SC
3,857
$582
$229
$328
$353
$690
$582
STANTON TELECOM INC.
371592
NE
1,089
$2,073
$844
$1,353
$1,229
$1,186
$2,030
STAR MEMBERSHIP CORP
230502
NC
16,205
$628
$323
$687
$305
$524
$628
STAR TEL CO
270441
LA
3,210
$1,340
$110
$381
$1,229
$966
$1,076
STAYTON COOP TEL CO
532399
OR
5,712
$841
$480
$849
$361
$806
$841
STEELVILLE TEL EXCH
421949
MO
4,211
$1,265
$547
$748
$718
$777
$1,265
STOCKBRIDGE & SHERWD
330954
WI
2,287
$607
$287
$313
$321
$525
$607
STOCKHOLM-STRANDBURG
391679
SD
597
$764
$409
$557
$355
$1,193
$764
STOUTLAND TEL CO
421951
MO
1,268
$725
$278
$353
$448
$762
$725
STRASBURG TEL CO
462207
CO
1,534
$677
$312
$434
$366
$590
$677
STRATFORD MUTUAL TEL
351305
IA
553
$1,385
$736
$1,358
$650
$1,403
$1,385
SUGAR VALLEY TEL CO
170206
PA
1,035
$555
$215
$244
$341
$737
$555
SUMMIT TEL & TEL -AK
613028
AK
252
$3,906
$1,324
$1,607
$2,581
$2,683
$3,906
SUNMAN TELECOMM CORP
320825
IN
4,355
$894
$309
$498
$585
$591
$894
SUREWEST TEL.
542334
CA
58,058
$542
$268
$249
$274
$342
$523
SW ARKANSAS TEL COOP
401724
AR
5,103
$1,243
$647
$805
$596
$901
$1,243
SW OKLAHOMA TEL CO
432025
OK
647
$634
$102
$634
$532
$1,975
$634
66

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

SWISHER TEL CO
351304
IA
748
$858
$783
$1,219
$75
$1,144
$858
SYCAMORE TEL CO
300658
OH
1,529
$351
$88
$337
$263
$826
$351
TABLE TOP TEL CO
453334
AZ
3,993
$1,405
$767
$1,423
$638
$1,448
$1,405
TATUM TEL CO
442150
TX
909
$777
$171
$510
$606
$1,159
$777
TAYLOR TEL CO-OP INC
442151
TX
6,173
$886
$466
$975
$419
$1,145
$886
TELLICO TEL CO
290578
TN
8,160
$466
$205
$304
$261
$495
$466
TENINO TELEPHONE CO
522446
WA
3,181
$976
$383
$353
$593
$685
$946
TENNESSEE TEL CO
290575
TN
47,085
$517
$253
$285
$264
$302
$517
TENNEY TEL CO
330958
WI
1,031
$542
$216
$382
$327
$642
$542
TERRAL TEL CO
432029
OK
215
$5,077
$1,837
$1,151
$3,240
$2,761
$3,912
THE BLAIR TEL CO
371524
NE
6,597
$573
$185
$283
$388
$456
$573
THE CHAMPAIGN TEL CO
300594
OH
7,103
$659
$272
$311
$387
$492
$659
THE CHILLICOTHE TEL
300597
OH
22,252
$900
$390
$371
$509
$386
$757
THE NOVA TEL CO
300644
OH
970
$1,225
$258
$464
$967
$819
$1,078
THE PONDEROSA TEL CO
542332
CA
8,435
$1,718
$865
$1,144
$853
$1,095
$1,718
THE SISKIYOU TEL CO
542339
CA
4,417
$1,993
$1,116
$1,761
$878
$1,175
$1,993
THREE RIVER TELCO
371525
NE
1,193
$1,547
$798
$1,279
$749
$1,447
$1,547
TOHONO O'ODHAM UTIL.
452173
AZ
3,803
$1,135
$634
$1,145
$501
$1,073
$1,135
TOLEDO TELEPHONE CO
522447
WA
1,912
$1,343
$639
$669
$704
$971
$1,343
TOPSHAM TEL CO
140068
VT
1,598
$1,178
$437
$587
$742
$645
$1,081
TOTAH COMMUNICATIONS
412030
KS
1,019
$1,679
$654
$930
$1,024
$1,916
$1,679
TOTAH COMMUNICATIONS
432030
OK
1,818
$1,089
$461
$851
$628
$1,564
$1,089
TOTELCOM COMM.
442060
TX
4,126
$742
$156
$476
$587
$891
$742
TOWNSHIP TEL CO
150129
NY
2,588
$709
$319
$256
$390
$496
$647
TRANS-CASCADES TEL
532378
OR
214
$1,633
$334
$728
$1,299
$2,552
$1,633
TRI COUNTY TEL ASSN
512296
WY
5,903
$1,677
$652
$679
$1,026
$693
$1,345
TRIANGLE TEL COOP
482257
MT
10,337
$1,324
$918
$1,737
$406
$695
$1,324
TRI-COUNTY COMM COOP
330960
WI
3,444
$987
$481
$566
$506
$636
$987
TRI-COUNTY TEL ASSN
411839
KS
2,849
$2,022
$1,054
$846
$968
$1,049
$1,814
TRI-COUNTY TEL CO-AR
401726
AR
5,863
$1,035
$359
$361
$676
$676
$1,035
TRUMANSBURG TEL CO.
150131
NY
4,451
$692
$296
$312
$396
$538
$692
67

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

TULAROSA BASIN TEL.
492265
NM
4,036
$1,508
$800
$992
$707
$1,033
$1,508
TWIN LAKES TEL COOP
290579
TN
33,878
$581
$342
$600
$239
$421
$581
TWIN VALLEY TEL INC
411840
KS
1,931
$1,213
$548
$824
$664
$1,289
$1,213
TWIN VALLEY-ULEN TEL
361491
MN
3,114
$787
$452
$495
$335
$606
$787
UBTA-UBET/STRATA
502287
UT
3,302
$1,192
$419
$524
$773
$992
$1,192
UNION RIVER TEL CO
100027
ME
1,211
$1,438
$710
$710
$728
$833
$1,438
UNION TEL CO
120049
NH
5,320
$332
$113
$246
$219
$456
$332
UNION TEL CO
330962
WI
3,875
$777
$412
$436
$366
$554
$777
UNION TELEPHONE CO
512297
WY
6,031
$501
$228
$733
$273
$759
$501
UNITED FARMERS TEL
351316
IA
547
$1,249
$685
$1,970
$564
$1,553
$1,249
UNITED TEL ASSN
411841
KS
4,767
$1,439
$751
$739
$688
$927
$1,427
UNITED TEL MUTUAL
381636
ND
10,082
$677
$386
$556
$291
$493
$677
UNITED UTILITIES INC
613023
AK
6,673
$749
$182
$500
$567
$1,015
$749
UNITEL, INC.
100029
ME
4,001
$597
$186
$240
$411
$448
$597
UPPER PENINSULA TEL
310732
MI
5,012
$868
$447
$706
$421
$719
$868
UTC OF TN
290581
TN
12,996
$832
$548
$669
$284
$635
$832
UTELCO, INC
330963
WI
12,453
$392
$157
$230
$235
$337
$392
VALLEY TEL COOP - NM
492176
NM
1,150
$2,461
$1,608
$2,271
$853
$2,255
$2,461
VALLEY TEL CO-OP -TX
442159
TX
5,765
$1,979
$1,037
$1,612
$942
$1,520
$1,979
VALLEY TEL COOP-AZ
452176
AZ
5,983
$1,745
$1,026
$1,524
$719
$1,233
$1,745
VALLEY TELECOMM.
391685
SD
3,190
$1,476
$892
$1,139
$584
$911
$1,476
VALLIANT TEL CO
432032
OK
1,654
$953
$389
$654
$564
$1,318
$953
VENTURE COMM. COOP
391680
SD
10,226
$958
$650
$765
$308
$613
$958
VERMONT TEL. CO-VT
147332
VT
17,646
$569
$187
$187
$382
$311
$499
VERNON TEL CO
150133
NY
1,758
$386
$165
$323
$221
$573
$386
VERNON TEL COOP
330966
WI
6,409
$612
$259
$510
$353
$546
$612
VOLCANO TEL CO
542343
CA
10,145
$777
$368
$458
$409
$608
$777
W. RIVER TELECOM.
381637
ND
14,324
$652
$369
$766
$283
$580
$652
W. WISCONSIN TELCOM
330971
WI
6,053
$1,290
$773
$775
$516
$546
$1,290
WABASH TEL COOP, INC
341088
IL
4,243
$787
$394
$701
$393
$887
$787
WAITSFIELD/FAYSTON
140069
VT
18,643
$538
$200
$211
$338
$284
$484
68

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

WALDRON TEL CO
310734
MI
481
$1,051
$296
$465
$755
$1,043
$1,051
WALNUT HILL TEL CO
401729
AR
4,351
$1,202
$274
$359
$928
$800
$1,074
WALNUT TEL CO, INC
351326
IA
682
$812
$387
$1,108
$425
$1,188
$812
WAMEGO TEL CO INC
411845
KS
4,672
$634
$251
$513
$383
$752
$634
WARREN TEL CO
100031
ME
1,226
$574
$156
$189
$418
$550
$574
WARWICK VALLEY-NJ
160135
NJ
5,688
$412
$183
$351
$230
$785
$412
WARWICK VALLEY-NY
150135
NY
9,336
$390
$192
$213
$198
$447
$390
WASHINGTON CTY RURAL
320834
IN
2,752
$751
$446
$634
$305
$672
$751
WAUNETA TEL CO
371597
NE
606
$1,998
$879
$584
$1,119
$1,109
$1,693
WAVERLY HALL, LLC
220392
GA
1,310
$905
$297
$711
$609
$1,121
$905
WEBB-DICKENS TEL
351327
IA
337
$2,488
$911
$2,012
$1,577
$1,828
$2,488
WEBSTER-CALHOUN COOP
351328
IA
4,117
$1,441
$1,064
$1,745
$377
$991
$1,441
WELLMAN COOP TEL
351329
IA
1,211
$760
$427
$723
$333
$922
$760
WEST CAROLINA RURAL
240550
SC
10,740
$1,804
$1,349
$1,190
$456
$802
$1,646
WEST CENTRAL TEL
361501
MN
3,502
$1,575
$1,013
$1,190
$562
$715
$1,575
WEST KENTUCKY RURAL
260421
KY
13,946
$926
$481
$528
$445
$515
$926
WEST LIBERTY TEL CO
351332
IA
3,257
$830
$489
$894
$341
$815
$830
WEST PENOBSCOT TEL
100034
ME
2,055
$363
$61
$140
$301
$408
$363
WEST RIVER COOP
391689
SD
3,420
$1,856
$1,269
$1,188
$587
$1,007
$1,775
WEST SIDE TEL-WV
200277
WV
2,340
$593
$177
$335
$416
$652
$593
WEST TENNESSEE TEL
290583
TN
3,255
$557
$248
$407
$309
$674
$557
WEST TEXAS RURAL TEL
442166
TX
1,895
$1,338
$203
$656
$1,135
$1,307
$1,338
WESTERN NEW MEXICO
492268
NM
6,217
$1,236
$478
$478
$757
$794
$1,236
WESTERN WAHKIAKUM
522451
WA
1,100
$2,022
$873
$749
$1,148
$1,378
$1,897
WES-TEX TEL CO-OP
442168
TX
2,255
$1,187
$429
$1,515
$758
$1,816
$1,187
WESTGATE dba WEAVTEL
520580
WA
20
$16,069
$6,404
$5,807
$9,666
$7,834
$13,641
WHEAT STATE TEL, INC
411847
KS
1,916
$1,096
$386
$501
$710
$1,033
$1,096
WHIDBEY TEL CO.
522452
WA
11,919
$560
$279
$301
$281
$420
$560
WIGGINS TEL ASSOC
462209
CO
1,511
$2,266
$1,468
$2,378
$799
$1,290
$2,266
WILKES MEMBERSHIP
230510
NC
9,723
$986
$636
$636
$349
$480
$986
WILKES TEL & ELC CO
220394
GA
9,354
$513
$211
$498
$303
$647
$513
69

Federal Communications Commission

DA 12-646

90%
90%

CPL used

Current

Capex

Current

Opex

to

Current

Capex

CPL

Opex

CPL

Determine

Study Area Name

SAC

State

Loops

CPL

CPL

Estimate

CPL

Estimate

Support

WILLISTON TEL CO
240551
SC
3,979
$587
$220
$260
$367
$590
$587
WILSON TEL CO INC
411849
KS
1,803
$2,031
$730
$1,002
$1,300
$1,361
$2,031
WILTON TEL CO - NH
120050
NH
2,589
$417
$134
$218
$283
$473
$417
WINN TEL CO
310737
MI
648
$723
$102
$268
$621
$856
$723
WINNEBAGO COOP-IA
351337
IA
5,567
$930
$614
$861
$316
$745
$930
WINNEBAGO COOP-MN
361337
MN
680
$779
$615
$591
$164
$1,107
$755
WINTERHAVEN TEL. CO.
542323
CA
994
$801
$206
$511
$595
$1,779
$801
WITTENBERG TEL CO
330973
WI
2,083
$672
$276
$389
$396
$606
$672
WOLVERINE TEL CO
310738
MI
7,398
$383
$136
$237
$247
$375
$383
WOOD COUNTY TEL CO
330974
WI
17,391
$571
$255
$255
$316
$343
$571
WOODHULL TEL CO
341091
IL
577
$681
$296
$693
$384
$1,179
$681
WOODSTOCK TEL CO
361510
MN
1,152
$1,555
$657
$1,204
$898
$1,208
$1,555
WYANDOTTE TEL CO
432034
OK
625
$712
$355
$403
$358
$1,475
$712
XIT RURAL TEL CO-OP
442170
TX
1,280
$2,355
$1,450
$1,450
$905
$1,753
$2,355
YELCOT TEL CO INC
401733
AR
2,698
$904
$402
$587
$502
$882
$904
YUKON TEL CO INC
613025
AK
481
$648
$120
$399
$528
$1,655
$648
ZENDA TEL COMPANY
411852
KS
162
$1,383
$50
$682
$1,333
$2,473
$1,383
70

Federal Communications Commission

DA 12-646

APPENDIX C

Specification for Study Area Boundary Submission

I.

General

Carriers may submit study area maps if they believe that the boundaries used by the FCC are not
representative. Maps must be submitted in ESRI compatible shapefile format such that each shapefile
represents a single study area. The shapefile must contain one data record for each exchange that
constitutes the study area. Each exchange should be represented as a closed, non-overlapping polygon
with the associated data fields described below. Submitted boundaries must be accompanied by metadata
or a plain text “readme” file containing the information listed below.
Since shapefiles typically consist of 3 to 9 individual files, the shapefile for the study area should be
submitted as a single, zipped file containing all the component files. The shapefile and encapsulating zip
file names must contain the company name and the 6-digit study area code. Shapefile and readme file
templates are available at http://www.fcc.gov/encyclopedia/rate-return-resources.
Materials must be sent by hand or messenger delivery. All filings must be addressed to the Commission’s
Secretary, Office of the Secretary, Federal Communications Commission. Attention: Lorenzo Miller,
202-418-0846 or John Emmett, 202-418-0386.
Hand-delivered or messenger-delivered paper filings for the Commission’s Secretary must be delivered to
FCC Headquarters at 445 12th St., SW, Room TW-A325, Washington, DC 20554. The filing hours are
8:00 a.m. to 7:00 p.m. All hand deliveries must be held together with rubber bands or fasteners. Any
envelopes and boxes must be disposed of before entering the building.
Note that submitted boundaries are public data and may be used in published FCC documents and
webpages.

II.

Shapefile

A shapefile template is available at http://www.fcc.gov/encyclopedia/rate-return-resources. Submitted
shapefiles must:
A. contain one closed, non-overlapping polygon for each exchange in the study area
B. have associated with each exchange polygon the following identifying data fields:
1. OCN – NECA-assigned operating company number as in the LERG
2. Company Name
3. Exchange Name
4. CLLI Code
5. Study Area Code
6. FRN (please use the FRN used for the 477 filing in the state)
C. have an assigned projection w/accompanying .prj file
D. use unprojected (geographic) WGS84 geographic coordinate system
E. have a minimum horizontal accuracy of +/- 40 feet or less, conforming to 1:24K national
mapping standards
F. be submitted as a WinZip archive with a name containing the company name and study area code
(e.g., CompanyName_123456.zip).
71

Federal Communications Commission

DA 12-646

III.

Readme File

A readme file template is available at http://www.fcc.gov/encyclopedia/rate-return-resources. The
readme file accompanying submitted boundaries must be submitted as a plain text file with a name
containing the relevant study area code (e.g., ReadMe_123456.txt). The readme file must contain the
following information:
A. Contact person name
B. Contact person address
C. Contact person phone number
D. Contact person email address
E. Date created/revised
F. Methodology – process steps to create the data
G. Certification statement including the name and contact information for the certifying company
officer.
72

Note: We are currently transitioning our documents into web compatible formats for easier reading. We have done our best to supply this content to you in a presentable form, but there may be some formatting issues while we improve the technology. The original version of the document is available as a PDF, Word Document, or as plain text.

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